{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Output\Hardwiring Committment.APPENDIX C.04-21-2023.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}22 Apr 2023, 11:14:27
{txt}
{com}. 
. 
. 
. 
. 
. 
. 
. 
. **** APPENDIX C STATISTICAL ANALYSES: INCLUSION OF POST-SELECTION/NOMINATION CHANGES TO AGENCY AGENDA STATUS INTERACTION WITH PRESIDENTIAL LOYALTY COVARIATE ****
. 
. 
. 
. 
. 
. ** RETRIEVE SINGLE EVENT RECORDS DATABASE [N = 860 APPOINTEE OBSERVATIONS: 831 UNCENSORED OBSERVATIONS; 29 CENSORED OBSERVATIONS] **
. 
. use "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Data\Krause and Byers.SRD.06-03-2022.dta", replace
{txt}
{com}. 
. 
. *
. *
. 
. 
. ** GENERATE CENSORING VARIABLE FOR HOLDOVER APPOINTEES SERVING BETWEEN/ACROSS ADMINISTRATIONS [=1]; UNCENSRED OBSERVATIONS [=0] ** 
. 
. gen singleadmin_service=1 if holdover==0
{txt}(29 missing values generated)

{com}. *
. replace singleadmin_service=0 if holdover==1
{txt}(29 real changes made)

{com}. *
. *
. tab singleadmin_service

{txt}singleadmin {c |}
   _service {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         29        3.37        3.37
{txt}          1 {c |}{res}        831       96.63      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        860      100.00
{txt}
{com}. 
. 
. ** SET FOR SURVIVAL DATA WITH A SINGLE RECORD PER APPOINTEE OBSERVATION [N = 860: UNCENSORED N = 831; CENSORED N = 29] ** 
. stset okapptdur, failure(singleadmin_service)

     {txt}failure event:  {res}singleadmin_service != 0 & singleadmin_service < .
{txt}obs. time interval:  {res}(0, okapptdur]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}        860{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}        860{txt}  observations remaining, representing
{res}        831{txt}  failures in single-record/single-failure data
{res}    850,034{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}    4,074
{txt}
{com}. 
. *
. *
. *
. 
. ** ESTIMATE COX SEMIPARAMETRIC AND WEIBULL PARAMETRIC MODELS PRESENTED IN MANUSCRIPT [TABLE 1: MODELS 1 - 4] ** 
. 
. ** NOTE COVARIATES THAT VARY TRHOUGH TIME ARE BASED ON THE STARTING DATE OF APPOINTED SERVICE [I.E., "OKSTART....""]
. 
. 
. 
. 
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. 
. *** COMPUTE TAB ON FULL SAMPLE OF N = 860 TO EVALUATE COMMONALITY OF SHORT TENURES ***
. 
. tab okapptdur

  {txt}okapptdur {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         20 {c |}{res}          1        0.12        0.12
{txt}         23 {c |}{res}          1        0.12        0.23
{txt}         67 {c |}{res}          1        0.12        0.35
{txt}        106 {c |}{res}          1        0.12        0.47
{txt}        120 {c |}{res}          1        0.12        0.58
{txt}        131 {c |}{res}          1        0.12        0.70
{txt}        133 {c |}{res}          1        0.12        0.81
{txt}        153 {c |}{res}          1        0.12        0.93
{txt}        178 {c |}{res}          1        0.12        1.05
{txt}        179 {c |}{res}          1        0.12        1.16
{txt}        184 {c |}{res}          1        0.12        1.28
{txt}        186 {c |}{res}          1        0.12        1.40
{txt}        187 {c |}{res}          1        0.12        1.51
{txt}        193 {c |}{res}          1        0.12        1.63
{txt}        198 {c |}{res}          2        0.23        1.86
{txt}        205 {c |}{res}          1        0.12        1.98
{txt}        206 {c |}{res}          2        0.23        2.21
{txt}        207 {c |}{res}          1        0.12        2.33
{txt}        209 {c |}{res}          2        0.23        2.56
{txt}        211 {c |}{res}          1        0.12        2.67
{txt}        214 {c |}{res}          1        0.12        2.79
{txt}        216 {c |}{res}          1        0.12        2.91
{txt}        217 {c |}{res}          1        0.12        3.02
{txt}        218 {c |}{res}          1        0.12        3.14
{txt}        223 {c |}{res}          1        0.12        3.26
{txt}        224 {c |}{res}          1        0.12        3.37
{txt}        226 {c |}{res}          1        0.12        3.49
{txt}        228 {c |}{res}          1        0.12        3.60
{txt}        233 {c |}{res}          2        0.23        3.84
{txt}        240 {c |}{res}          2        0.23        4.07
{txt}        241 {c |}{res}          1        0.12        4.19
{txt}        244 {c |}{res}          1        0.12        4.30
{txt}        247 {c |}{res}          3        0.35        4.65
{txt}        250 {c |}{res}          2        0.23        4.88
{txt}        253 {c |}{res}          1        0.12        5.00
{txt}        259 {c |}{res}          1        0.12        5.12
{txt}        264 {c |}{res}          1        0.12        5.23
{txt}        272 {c |}{res}          2        0.23        5.47
{txt}        278 {c |}{res}          1        0.12        5.58
{txt}        279 {c |}{res}          2        0.23        5.81
{txt}        280 {c |}{res}          1        0.12        5.93
{txt}        283 {c |}{res}          2        0.23        6.16
{txt}        284 {c |}{res}          1        0.12        6.28
{txt}        288 {c |}{res}          2        0.23        6.51
{txt}        289 {c |}{res}          1        0.12        6.63
{txt}        291 {c |}{res}          1        0.12        6.74
{txt}        292 {c |}{res}          2        0.23        6.98
{txt}        295 {c |}{res}          1        0.12        7.09
{txt}        299 {c |}{res}          2        0.23        7.33
{txt}        302 {c |}{res}          2        0.23        7.56
{txt}        306 {c |}{res}          1        0.12        7.67
{txt}        307 {c |}{res}          1        0.12        7.79
{txt}        308 {c |}{res}          1        0.12        7.91
{txt}        310 {c |}{res}          1        0.12        8.02
{txt}        311 {c |}{res}          1        0.12        8.14
{txt}        312 {c |}{res}          1        0.12        8.26
{txt}        315 {c |}{res}          1        0.12        8.37
{txt}        316 {c |}{res}          1        0.12        8.49
{txt}        319 {c |}{res}          2        0.23        8.72
{txt}        320 {c |}{res}          1        0.12        8.84
{txt}        322 {c |}{res}          2        0.23        9.07
{txt}        323 {c |}{res}          1        0.12        9.19
{txt}        326 {c |}{res}          1        0.12        9.30
{txt}        327 {c |}{res}          2        0.23        9.53
{txt}        330 {c |}{res}          2        0.23        9.77
{txt}        331 {c |}{res}          3        0.35       10.12
{txt}        335 {c |}{res}          3        0.35       10.47
{txt}        336 {c |}{res}          2        0.23       10.70
{txt}        343 {c |}{res}          1        0.12       10.81
{txt}        347 {c |}{res}          1        0.12       10.93
{txt}        349 {c |}{res}          1        0.12       11.05
{txt}        351 {c |}{res}          1        0.12       11.16
{txt}        353 {c |}{res}          1        0.12       11.28
{txt}        358 {c |}{res}          1        0.12       11.40
{txt}        359 {c |}{res}          1        0.12       11.51
{txt}        364 {c |}{res}          1        0.12       11.63
{txt}        365 {c |}{res}          2        0.23       11.86
{txt}        367 {c |}{res}          1        0.12       11.98
{txt}        370 {c |}{res}          1        0.12       12.09
{txt}        371 {c |}{res}          3        0.35       12.44
{txt}        372 {c |}{res}          1        0.12       12.56
{txt}        373 {c |}{res}          2        0.23       12.79
{txt}        377 {c |}{res}          1        0.12       12.91
{txt}        379 {c |}{res}          1        0.12       13.02
{txt}        382 {c |}{res}          1        0.12       13.14
{txt}        383 {c |}{res}          2        0.23       13.37
{txt}        390 {c |}{res}          2        0.23       13.60
{txt}        393 {c |}{res}          1        0.12       13.72
{txt}        394 {c |}{res}          3        0.35       14.07
{txt}        396 {c |}{res}          1        0.12       14.19
{txt}        397 {c |}{res}          1        0.12       14.30
{txt}        401 {c |}{res}          1        0.12       14.42
{txt}        403 {c |}{res}          2        0.23       14.65
{txt}        404 {c |}{res}          1        0.12       14.77
{txt}        405 {c |}{res}          1        0.12       14.88
{txt}        407 {c |}{res}          1        0.12       15.00
{txt}        410 {c |}{res}          2        0.23       15.23
{txt}        411 {c |}{res}          1        0.12       15.35
{txt}        413 {c |}{res}          1        0.12       15.47
{txt}        415 {c |}{res}          1        0.12       15.58
{txt}        416 {c |}{res}          1        0.12       15.70
{txt}        417 {c |}{res}          1        0.12       15.81
{txt}        420 {c |}{res}          1        0.12       15.93
{txt}        421 {c |}{res}          1        0.12       16.05
{txt}        422 {c |}{res}          1        0.12       16.16
{txt}        423 {c |}{res}          1        0.12       16.28
{txt}        424 {c |}{res}          2        0.23       16.51
{txt}        425 {c |}{res}          2        0.23       16.74
{txt}        426 {c |}{res}          1        0.12       16.86
{txt}        427 {c |}{res}          2        0.23       17.09
{txt}        428 {c |}{res}          1        0.12       17.21
{txt}        430 {c |}{res}          2        0.23       17.44
{txt}        432 {c |}{res}          1        0.12       17.56
{txt}        433 {c |}{res}          1        0.12       17.67
{txt}        434 {c |}{res}          2        0.23       17.91
{txt}        436 {c |}{res}          1        0.12       18.02
{txt}        438 {c |}{res}          1        0.12       18.14
{txt}        440 {c |}{res}          1        0.12       18.26
{txt}        446 {c |}{res}          1        0.12       18.37
{txt}        447 {c |}{res}          2        0.23       18.60
{txt}        450 {c |}{res}          1        0.12       18.72
{txt}        454 {c |}{res}          2        0.23       18.95
{txt}        457 {c |}{res}          4        0.47       19.42
{txt}        459 {c |}{res}          1        0.12       19.53
{txt}        461 {c |}{res}          2        0.23       19.77
{txt}        463 {c |}{res}          1        0.12       19.88
{txt}        465 {c |}{res}          1        0.12       20.00
{txt}        470 {c |}{res}          1        0.12       20.12
{txt}        471 {c |}{res}          1        0.12       20.23
{txt}        472 {c |}{res}          1        0.12       20.35
{txt}        473 {c |}{res}          1        0.12       20.47
{txt}        474 {c |}{res}          1        0.12       20.58
{txt}        475 {c |}{res}          1        0.12       20.70
{txt}        483 {c |}{res}          1        0.12       20.81
{txt}        484 {c |}{res}          1        0.12       20.93
{txt}        485 {c |}{res}          1        0.12       21.05
{txt}        487 {c |}{res}          1        0.12       21.16
{txt}        490 {c |}{res}          1        0.12       21.28
{txt}        497 {c |}{res}          1        0.12       21.40
{txt}        498 {c |}{res}          1        0.12       21.51
{txt}        502 {c |}{res}          2        0.23       21.74
{txt}        504 {c |}{res}          1        0.12       21.86
{txt}        505 {c |}{res}          1        0.12       21.98
{txt}        506 {c |}{res}          1        0.12       22.09
{txt}        507 {c |}{res}          1        0.12       22.21
{txt}        508 {c |}{res}          1        0.12       22.33
{txt}        511 {c |}{res}          2        0.23       22.56
{txt}        515 {c |}{res}          2        0.23       22.79
{txt}        516 {c |}{res}          2        0.23       23.02
{txt}        518 {c |}{res}          1        0.12       23.14
{txt}        519 {c |}{res}          3        0.35       23.49
{txt}        520 {c |}{res}          1        0.12       23.60
{txt}        522 {c |}{res}          3        0.35       23.95
{txt}        524 {c |}{res}          1        0.12       24.07
{txt}        525 {c |}{res}          1        0.12       24.19
{txt}        526 {c |}{res}          1        0.12       24.30
{txt}        527 {c |}{res}          2        0.23       24.53
{txt}        530 {c |}{res}          1        0.12       24.65
{txt}        531 {c |}{res}          3        0.35       25.00
{txt}        532 {c |}{res}          5        0.58       25.58
{txt}        533 {c |}{res}          1        0.12       25.70
{txt}        536 {c |}{res}          3        0.35       26.05
{txt}        537 {c |}{res}          1        0.12       26.16
{txt}        538 {c |}{res}          1        0.12       26.28
{txt}        540 {c |}{res}          1        0.12       26.40
{txt}        541 {c |}{res}          1        0.12       26.51
{txt}        543 {c |}{res}          1        0.12       26.63
{txt}        544 {c |}{res}          1        0.12       26.74
{txt}        545 {c |}{res}          1        0.12       26.86
{txt}        550 {c |}{res}          1        0.12       26.98
{txt}        551 {c |}{res}          1        0.12       27.09
{txt}        552 {c |}{res}          1        0.12       27.21
{txt}        554 {c |}{res}          1        0.12       27.33
{txt}        556 {c |}{res}          4        0.47       27.79
{txt}        559 {c |}{res}          1        0.12       27.91
{txt}        563 {c |}{res}          1        0.12       28.02
{txt}        564 {c |}{res}          1        0.12       28.14
{txt}        566 {c |}{res}          1        0.12       28.26
{txt}        568 {c |}{res}          1        0.12       28.37
{txt}        569 {c |}{res}          1        0.12       28.49
{txt}        571 {c |}{res}          1        0.12       28.60
{txt}        573 {c |}{res}          1        0.12       28.72
{txt}        574 {c |}{res}          1        0.12       28.84
{txt}        575 {c |}{res}          1        0.12       28.95
{txt}        578 {c |}{res}          1        0.12       29.07
{txt}        580 {c |}{res}          1        0.12       29.19
{txt}        582 {c |}{res}          1        0.12       29.30
{txt}        587 {c |}{res}          1        0.12       29.42
{txt}        589 {c |}{res}          1        0.12       29.53
{txt}        590 {c |}{res}          1        0.12       29.65
{txt}        594 {c |}{res}          2        0.23       29.88
{txt}        595 {c |}{res}          1        0.12       30.00
{txt}        598 {c |}{res}          2        0.23       30.23
{txt}        601 {c |}{res}          5        0.58       30.81
{txt}        602 {c |}{res}          2        0.23       31.05
{txt}        603 {c |}{res}          1        0.12       31.16
{txt}        604 {c |}{res}          1        0.12       31.28
{txt}        606 {c |}{res}          1        0.12       31.40
{txt}        607 {c |}{res}          1        0.12       31.51
{txt}        608 {c |}{res}          1        0.12       31.63
{txt}        609 {c |}{res}          1        0.12       31.74
{txt}        610 {c |}{res}          1        0.12       31.86
{txt}        614 {c |}{res}          1        0.12       31.98
{txt}        617 {c |}{res}          1        0.12       32.09
{txt}        622 {c |}{res}          1        0.12       32.21
{txt}        626 {c |}{res}          1        0.12       32.33
{txt}        630 {c |}{res}          1        0.12       32.44
{txt}        634 {c |}{res}          1        0.12       32.56
{txt}        636 {c |}{res}          1        0.12       32.67
{txt}        638 {c |}{res}          2        0.23       32.91
{txt}        646 {c |}{res}          1        0.12       33.02
{txt}        648 {c |}{res}          1        0.12       33.14
{txt}        651 {c |}{res}          1        0.12       33.26
{txt}        653 {c |}{res}          1        0.12       33.37
{txt}        654 {c |}{res}          2        0.23       33.60
{txt}        656 {c |}{res}          1        0.12       33.72
{txt}        657 {c |}{res}          1        0.12       33.84
{txt}        658 {c |}{res}          1        0.12       33.95
{txt}        660 {c |}{res}          1        0.12       34.07
{txt}        665 {c |}{res}          1        0.12       34.19
{txt}        667 {c |}{res}          1        0.12       34.30
{txt}        671 {c |}{res}          1        0.12       34.42
{txt}        674 {c |}{res}          3        0.35       34.77
{txt}        678 {c |}{res}          1        0.12       34.88
{txt}        679 {c |}{res}          1        0.12       35.00
{txt}        684 {c |}{res}          1        0.12       35.12
{txt}        686 {c |}{res}          1        0.12       35.23
{txt}        688 {c |}{res}          1        0.12       35.35
{txt}        690 {c |}{res}          1        0.12       35.47
{txt}        694 {c |}{res}          3        0.35       35.81
{txt}        696 {c |}{res}          2        0.23       36.05
{txt}        699 {c |}{res}          1        0.12       36.16
{txt}        700 {c |}{res}          3        0.35       36.51
{txt}        702 {c |}{res}          1        0.12       36.63
{txt}        704 {c |}{res}          1        0.12       36.74
{txt}        706 {c |}{res}          2        0.23       36.98
{txt}        708 {c |}{res}          2        0.23       37.21
{txt}        709 {c |}{res}          1        0.12       37.33
{txt}        710 {c |}{res}          2        0.23       37.56
{txt}        712 {c |}{res}          1        0.12       37.67
{txt}        717 {c |}{res}          2        0.23       37.91
{txt}        718 {c |}{res}          1        0.12       38.02
{txt}        725 {c |}{res}          1        0.12       38.14
{txt}        734 {c |}{res}          1        0.12       38.26
{txt}        738 {c |}{res}          1        0.12       38.37
{txt}        739 {c |}{res}          1        0.12       38.49
{txt}        740 {c |}{res}          2        0.23       38.72
{txt}        741 {c |}{res}          1        0.12       38.84
{txt}        742 {c |}{res}          4        0.47       39.30
{txt}        743 {c |}{res}          1        0.12       39.42
{txt}        749 {c |}{res}          2        0.23       39.65
{txt}        750 {c |}{res}          1        0.12       39.77
{txt}        753 {c |}{res}          1        0.12       39.88
{txt}        754 {c |}{res}          2        0.23       40.12
{txt}        757 {c |}{res}          3        0.35       40.47
{txt}        759 {c |}{res}          1        0.12       40.58
{txt}        761 {c |}{res}          1        0.12       40.70
{txt}        764 {c |}{res}          1        0.12       40.81
{txt}        765 {c |}{res}          1        0.12       40.93
{txt}        769 {c |}{res}          2        0.23       41.16
{txt}        770 {c |}{res}          1        0.12       41.28
{txt}        771 {c |}{res}          4        0.47       41.74
{txt}        772 {c |}{res}          2        0.23       41.98
{txt}        773 {c |}{res}          1        0.12       42.09
{txt}        776 {c |}{res}          1        0.12       42.21
{txt}        779 {c |}{res}          1        0.12       42.33
{txt}        780 {c |}{res}          2        0.23       42.56
{txt}        781 {c |}{res}          1        0.12       42.67
{txt}        785 {c |}{res}          2        0.23       42.91
{txt}        792 {c |}{res}          1        0.12       43.02
{txt}        794 {c |}{res}          1        0.12       43.14
{txt}        796 {c |}{res}          1        0.12       43.26
{txt}        797 {c |}{res}          3        0.35       43.60
{txt}        798 {c |}{res}          1        0.12       43.72
{txt}        803 {c |}{res}          1        0.12       43.84
{txt}        812 {c |}{res}          2        0.23       44.07
{txt}        815 {c |}{res}          1        0.12       44.19
{txt}        816 {c |}{res}          2        0.23       44.42
{txt}        817 {c |}{res}          3        0.35       44.77
{txt}        818 {c |}{res}          1        0.12       44.88
{txt}        820 {c |}{res}          1        0.12       45.00
{txt}        821 {c |}{res}          1        0.12       45.12
{txt}        822 {c |}{res}          1        0.12       45.23
{txt}        824 {c |}{res}          1        0.12       45.35
{txt}        825 {c |}{res}          1        0.12       45.47
{txt}        829 {c |}{res}          1        0.12       45.58
{txt}        835 {c |}{res}          1        0.12       45.70
{txt}        836 {c |}{res}          1        0.12       45.81
{txt}        839 {c |}{res}          1        0.12       45.93
{txt}        840 {c |}{res}          1        0.12       46.05
{txt}        843 {c |}{res}          1        0.12       46.16
{txt}        844 {c |}{res}          1        0.12       46.28
{txt}        845 {c |}{res}          1        0.12       46.40
{txt}        847 {c |}{res}          1        0.12       46.51
{txt}        852 {c |}{res}          1        0.12       46.63
{txt}        853 {c |}{res}          1        0.12       46.74
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{txt}       2907 {c |}{res}          1        0.12       98.72
{txt}       2911 {c |}{res}          1        0.12       98.84
{txt}       2919 {c |}{res}          1        0.12       98.95
{txt}       2920 {c |}{res}          3        0.35       99.30
{txt}       2921 {c |}{res}          1        0.12       99.42
{txt}       2992 {c |}{res}          1        0.12       99.53
{txt}       3118 {c |}{res}          1        0.12       99.65
{txt}       3229 {c |}{res}          1        0.12       99.77
{txt}       3523 {c |}{res}          1        0.12       99.88
{txt}       4074 {c |}{res}          1        0.12      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        860      100.00
{txt}
{com}. 
. *
. *
. *
. 
. *** COMPUTE TAB ON TRUNCATED SAMPLE EXCLUDING LAST TWO YEARS OF EACH PRESIDENTIAL TERM: N = 610 [70.93% OF FULL SAMPLE] TO EVALUATE COMMONALITY OF SHORT TENURES ***
. 
. tab okapptdur if okstartadyr==1 | okstartadyr==2 | okstartadyr==5 | okstartadyr==6

  {txt}okapptdur {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         67 {c |}{res}          1        0.16        0.16
{txt}        120 {c |}{res}          1        0.16        0.33
{txt}        193 {c |}{res}          1        0.16        0.49
{txt}        198 {c |}{res}          1        0.16        0.66
{txt}        205 {c |}{res}          1        0.16        0.82
{txt}        206 {c |}{res}          1        0.16        0.98
{txt}        209 {c |}{res}          1        0.16        1.15
{txt}        223 {c |}{res}          1        0.16        1.31
{txt}        224 {c |}{res}          1        0.16        1.48
{txt}        240 {c |}{res}          1        0.16        1.64
{txt}        241 {c |}{res}          1        0.16        1.80
{txt}        247 {c |}{res}          1        0.16        1.97
{txt}        259 {c |}{res}          1        0.16        2.13
{txt}        279 {c |}{res}          1        0.16        2.30
{txt}        283 {c |}{res}          1        0.16        2.46
{txt}        284 {c |}{res}          1        0.16        2.62
{txt}        288 {c |}{res}          1        0.16        2.79
{txt}        289 {c |}{res}          1        0.16        2.95
{txt}        292 {c |}{res}          1        0.16        3.11
{txt}        299 {c |}{res}          2        0.33        3.44
{txt}        306 {c |}{res}          1        0.16        3.61
{txt}        307 {c |}{res}          1        0.16        3.77
{txt}        308 {c |}{res}          1        0.16        3.93
{txt}        310 {c |}{res}          1        0.16        4.10
{txt}        311 {c |}{res}          1        0.16        4.26
{txt}        316 {c |}{res}          1        0.16        4.43
{txt}        319 {c |}{res}          1        0.16        4.59
{txt}        322 {c |}{res}          2        0.33        4.92
{txt}        323 {c |}{res}          1        0.16        5.08
{txt}        326 {c |}{res}          1        0.16        5.25
{txt}        331 {c |}{res}          2        0.33        5.57
{txt}        335 {c |}{res}          2        0.33        5.90
{txt}        336 {c |}{res}          1        0.16        6.07
{txt}        343 {c |}{res}          1        0.16        6.23
{txt}        364 {c |}{res}          1        0.16        6.39
{txt}        367 {c |}{res}          1        0.16        6.56
{txt}        371 {c |}{res}          1        0.16        6.72
{txt}        372 {c |}{res}          1        0.16        6.89
{txt}        373 {c |}{res}          1        0.16        7.05
{txt}        377 {c |}{res}          1        0.16        7.21
{txt}        379 {c |}{res}          1        0.16        7.38
{txt}        382 {c |}{res}          1        0.16        7.54
{txt}        390 {c |}{res}          1        0.16        7.70
{txt}        394 {c |}{res}          1        0.16        7.87
{txt}        401 {c |}{res}          1        0.16        8.03
{txt}        403 {c |}{res}          1        0.16        8.20
{txt}        404 {c |}{res}          1        0.16        8.36
{txt}        405 {c |}{res}          1        0.16        8.52
{txt}        422 {c |}{res}          1        0.16        8.69
{txt}        425 {c |}{res}          1        0.16        8.85
{txt}        427 {c |}{res}          2        0.33        9.18
{txt}        428 {c |}{res}          1        0.16        9.34
{txt}        433 {c |}{res}          1        0.16        9.51
{txt}        434 {c |}{res}          2        0.33        9.84
{txt}        436 {c |}{res}          1        0.16       10.00
{txt}        457 {c |}{res}          1        0.16       10.16
{txt}        465 {c |}{res}          1        0.16       10.33
{txt}        470 {c |}{res}          1        0.16       10.49
{txt}        471 {c |}{res}          1        0.16       10.66
{txt}        474 {c |}{res}          1        0.16       10.82
{txt}        475 {c |}{res}          1        0.16       10.98
{txt}        483 {c |}{res}          1        0.16       11.15
{txt}        485 {c |}{res}          1        0.16       11.31
{txt}        490 {c |}{res}          1        0.16       11.48
{txt}        498 {c |}{res}          1        0.16       11.64
{txt}        502 {c |}{res}          1        0.16       11.80
{txt}        505 {c |}{res}          1        0.16       11.97
{txt}        507 {c |}{res}          1        0.16       12.13
{txt}        508 {c |}{res}          1        0.16       12.30
{txt}        515 {c |}{res}          1        0.16       12.46
{txt}        516 {c |}{res}          1        0.16       12.62
{txt}        518 {c |}{res}          1        0.16       12.79
{txt}        519 {c |}{res}          2        0.33       13.11
{txt}        520 {c |}{res}          1        0.16       13.28
{txt}        522 {c |}{res}          1        0.16       13.44
{txt}        525 {c |}{res}          1        0.16       13.61
{txt}        527 {c |}{res}          2        0.33       13.93
{txt}        530 {c |}{res}          1        0.16       14.10
{txt}        532 {c |}{res}          2        0.33       14.43
{txt}        541 {c |}{res}          1        0.16       14.59
{txt}        545 {c |}{res}          1        0.16       14.75
{txt}        556 {c |}{res}          3        0.49       15.25
{txt}        559 {c |}{res}          1        0.16       15.41
{txt}        569 {c |}{res}          1        0.16       15.57
{txt}        571 {c |}{res}          1        0.16       15.74
{txt}        574 {c |}{res}          1        0.16       15.90
{txt}        578 {c |}{res}          1        0.16       16.07
{txt}        582 {c |}{res}          1        0.16       16.23
{txt}        590 {c |}{res}          1        0.16       16.39
{txt}        594 {c |}{res}          1        0.16       16.56
{txt}        595 {c |}{res}          1        0.16       16.72
{txt}        598 {c |}{res}          1        0.16       16.89
{txt}        601 {c |}{res}          2        0.33       17.21
{txt}        606 {c |}{res}          1        0.16       17.38
{txt}        608 {c |}{res}          1        0.16       17.54
{txt}        610 {c |}{res}          1        0.16       17.70
{txt}        630 {c |}{res}          1        0.16       17.87
{txt}        634 {c |}{res}          1        0.16       18.03
{txt}        638 {c |}{res}          2        0.33       18.36
{txt}        651 {c |}{res}          1        0.16       18.52
{txt}        653 {c |}{res}          1        0.16       18.69
{txt}        654 {c |}{res}          1        0.16       18.85
{txt}        656 {c |}{res}          1        0.16       19.02
{txt}        657 {c |}{res}          1        0.16       19.18
{txt}        658 {c |}{res}          1        0.16       19.34
{txt}        660 {c |}{res}          1        0.16       19.51
{txt}        667 {c |}{res}          1        0.16       19.67
{txt}        671 {c |}{res}          1        0.16       19.84
{txt}        674 {c |}{res}          1        0.16       20.00
{txt}        678 {c |}{res}          1        0.16       20.16
{txt}        679 {c |}{res}          1        0.16       20.33
{txt}        688 {c |}{res}          1        0.16       20.49
{txt}        694 {c |}{res}          2        0.33       20.82
{txt}        696 {c |}{res}          2        0.33       21.15
{txt}        699 {c |}{res}          1        0.16       21.31
{txt}        700 {c |}{res}          3        0.49       21.80
{txt}        704 {c |}{res}          1        0.16       21.97
{txt}        706 {c |}{res}          2        0.33       22.30
{txt}        708 {c |}{res}          2        0.33       22.62
{txt}        709 {c |}{res}          1        0.16       22.79
{txt}        710 {c |}{res}          2        0.33       23.11
{txt}        717 {c |}{res}          1        0.16       23.28
{txt}        718 {c |}{res}          1        0.16       23.44
{txt}        725 {c |}{res}          1        0.16       23.61
{txt}        734 {c |}{res}          1        0.16       23.77
{txt}        738 {c |}{res}          1        0.16       23.93
{txt}        741 {c |}{res}          1        0.16       24.10
{txt}        742 {c |}{res}          3        0.49       24.59
{txt}        743 {c |}{res}          1        0.16       24.75
{txt}        749 {c |}{res}          2        0.33       25.08
{txt}        750 {c |}{res}          1        0.16       25.25
{txt}        753 {c |}{res}          1        0.16       25.41
{txt}        754 {c |}{res}          1        0.16       25.57
{txt}        757 {c |}{res}          2        0.33       25.90
{txt}        761 {c |}{res}          1        0.16       26.07
{txt}        764 {c |}{res}          1        0.16       26.23
{txt}        765 {c |}{res}          1        0.16       26.39
{txt}        769 {c |}{res}          2        0.33       26.72
{txt}        770 {c |}{res}          1        0.16       26.89
{txt}        771 {c |}{res}          4        0.66       27.54
{txt}        772 {c |}{res}          2        0.33       27.87
{txt}        773 {c |}{res}          1        0.16       28.03
{txt}        776 {c |}{res}          1        0.16       28.20
{txt}        779 {c |}{res}          1        0.16       28.36
{txt}        780 {c |}{res}          1        0.16       28.52
{txt}        781 {c |}{res}          1        0.16       28.69
{txt}        785 {c |}{res}          2        0.33       29.02
{txt}        796 {c |}{res}          1        0.16       29.18
{txt}        797 {c |}{res}          2        0.33       29.51
{txt}        798 {c |}{res}          1        0.16       29.67
{txt}        803 {c |}{res}          1        0.16       29.84
{txt}        812 {c |}{res}          2        0.33       30.16
{txt}        815 {c |}{res}          1        0.16       30.33
{txt}        816 {c |}{res}          1        0.16       30.49
{txt}        817 {c |}{res}          2        0.33       30.82
{txt}        820 {c |}{res}          1        0.16       30.98
{txt}        821 {c |}{res}          1        0.16       31.15
{txt}        822 {c |}{res}          1        0.16       31.31
{txt}        824 {c |}{res}          1        0.16       31.48
{txt}        825 {c |}{res}          1        0.16       31.64
{txt}        829 {c |}{res}          1        0.16       31.80
{txt}        835 {c |}{res}          1        0.16       31.97
{txt}        836 {c |}{res}          1        0.16       32.13
{txt}        839 {c |}{res}          1        0.16       32.30
{txt}        843 {c |}{res}          1        0.16       32.46
{txt}        844 {c |}{res}          1        0.16       32.62
{txt}        847 {c |}{res}          1        0.16       32.79
{txt}        852 {c |}{res}          1        0.16       32.95
{txt}        853 {c |}{res}          1        0.16       33.11
{txt}        855 {c |}{res}          1        0.16       33.28
{txt}        863 {c |}{res}          1        0.16       33.44
{txt}        867 {c |}{res}          1        0.16       33.61
{txt}        877 {c |}{res}          2        0.33       33.93
{txt}        881 {c |}{res}          1        0.16       34.10
{txt}        884 {c |}{res}          1        0.16       34.26
{txt}        885 {c |}{res}          1        0.16       34.43
{txt}        886 {c |}{res}          1        0.16       34.59
{txt}        887 {c |}{res}          1        0.16       34.75
{txt}        889 {c |}{res}          3        0.49       35.25
{txt}        891 {c |}{res}          1        0.16       35.41
{txt}        893 {c |}{res}          1        0.16       35.57
{txt}        897 {c |}{res}          1        0.16       35.74
{txt}        898 {c |}{res}          1        0.16       35.90
{txt}        900 {c |}{res}          1        0.16       36.07
{txt}        901 {c |}{res}          1        0.16       36.23
{txt}        902 {c |}{res}          2        0.33       36.56
{txt}        903 {c |}{res}          1        0.16       36.72
{txt}        904 {c |}{res}          1        0.16       36.89
{txt}        907 {c |}{res}          3        0.49       37.38
{txt}        910 {c |}{res}          1        0.16       37.54
{txt}        912 {c |}{res}          1        0.16       37.70
{txt}        915 {c |}{res}          1        0.16       37.87
{txt}        920 {c |}{res}          2        0.33       38.20
{txt}        921 {c |}{res}          1        0.16       38.36
{txt}        922 {c |}{res}          2        0.33       38.69
{txt}        923 {c |}{res}          1        0.16       38.85
{txt}        925 {c |}{res}          1        0.16       39.02
{txt}        931 {c |}{res}          1        0.16       39.18
{txt}        932 {c |}{res}          1        0.16       39.34
{txt}        933 {c |}{res}          1        0.16       39.51
{txt}        935 {c |}{res}          3        0.49       40.00
{txt}        936 {c |}{res}          1        0.16       40.16
{txt}        937 {c |}{res}          1        0.16       40.33
{txt}        939 {c |}{res}          1        0.16       40.49
{txt}        940 {c |}{res}          1        0.16       40.66
{txt}        942 {c |}{res}          2        0.33       40.98
{txt}        943 {c |}{res}          2        0.33       41.31
{txt}        947 {c |}{res}          1        0.16       41.48
{txt}        949 {c |}{res}          2        0.33       41.80
{txt}        950 {c |}{res}          1        0.16       41.97
{txt}        954 {c |}{res}          1        0.16       42.13
{txt}        955 {c |}{res}          1        0.16       42.30
{txt}        956 {c |}{res}          1        0.16       42.46
{txt}        957 {c |}{res}          2        0.33       42.79
{txt}        959 {c |}{res}          1        0.16       42.95
{txt}        960 {c |}{res}          2        0.33       43.28
{txt}        963 {c |}{res}          1        0.16       43.44
{txt}        966 {c |}{res}          2        0.33       43.77
{txt}        967 {c |}{res}          1        0.16       43.93
{txt}        970 {c |}{res}          2        0.33       44.26
{txt}        971 {c |}{res}          1        0.16       44.43
{txt}        977 {c |}{res}          1        0.16       44.59
{txt}        979 {c |}{res}          1        0.16       44.75
{txt}        989 {c |}{res}          1        0.16       44.92
{txt}        990 {c |}{res}          1        0.16       45.08
{txt}        991 {c |}{res}          1        0.16       45.25
{txt}        993 {c |}{res}          2        0.33       45.57
{txt}        996 {c |}{res}          1        0.16       45.74
{txt}       1004 {c |}{res}          2        0.33       46.07
{txt}       1007 {c |}{res}          2        0.33       46.39
{txt}       1009 {c |}{res}          1        0.16       46.56
{txt}       1014 {c |}{res}          1        0.16       46.72
{txt}       1015 {c |}{res}          1        0.16       46.89
{txt}       1019 {c |}{res}          1        0.16       47.05
{txt}       1025 {c |}{res}          1        0.16       47.21
{txt}       1029 {c |}{res}          1        0.16       47.38
{txt}       1034 {c |}{res}          1        0.16       47.54
{txt}       1035 {c |}{res}          1        0.16       47.70
{txt}       1038 {c |}{res}          1        0.16       47.87
{txt}       1043 {c |}{res}          1        0.16       48.03
{txt}       1044 {c |}{res}          1        0.16       48.20
{txt}       1047 {c |}{res}          1        0.16       48.36
{txt}       1050 {c |}{res}          1        0.16       48.52
{txt}       1051 {c |}{res}          1        0.16       48.69
{txt}       1052 {c |}{res}          3        0.49       49.18
{txt}       1053 {c |}{res}          1        0.16       49.34
{txt}       1056 {c |}{res}          2        0.33       49.67
{txt}       1058 {c |}{res}          2        0.33       50.00
{txt}       1062 {c |}{res}          1        0.16       50.16
{txt}       1074 {c |}{res}          1        0.16       50.33
{txt}       1075 {c |}{res}          1        0.16       50.49
{txt}       1077 {c |}{res}          1        0.16       50.66
{txt}       1078 {c |}{res}          4        0.66       51.31
{txt}       1084 {c |}{res}          1        0.16       51.48
{txt}       1086 {c |}{res}          3        0.49       51.97
{txt}       1088 {c |}{res}          1        0.16       52.13
{txt}       1090 {c |}{res}          1        0.16       52.30
{txt}       1091 {c |}{res}          2        0.33       52.62
{txt}       1100 {c |}{res}          1        0.16       52.79
{txt}       1102 {c |}{res}          1        0.16       52.95
{txt}       1106 {c |}{res}          2        0.33       53.28
{txt}       1109 {c |}{res}          1        0.16       53.44
{txt}       1120 {c |}{res}          1        0.16       53.61
{txt}       1124 {c |}{res}          1        0.16       53.77
{txt}       1128 {c |}{res}          1        0.16       53.93
{txt}       1129 {c |}{res}          1        0.16       54.10
{txt}       1134 {c |}{res}          3        0.49       54.59
{txt}       1135 {c |}{res}          1        0.16       54.75
{txt}       1138 {c |}{res}          1        0.16       54.92
{txt}       1142 {c |}{res}          1        0.16       55.08
{txt}       1143 {c |}{res}          1        0.16       55.25
{txt}       1146 {c |}{res}          1        0.16       55.41
{txt}       1152 {c |}{res}          1        0.16       55.57
{txt}       1155 {c |}{res}          1        0.16       55.74
{txt}       1156 {c |}{res}          2        0.33       56.07
{txt}       1157 {c |}{res}          3        0.49       56.56
{txt}       1158 {c |}{res}          2        0.33       56.89
{txt}       1160 {c |}{res}          1        0.16       57.05
{txt}       1161 {c |}{res}          2        0.33       57.38
{txt}       1164 {c |}{res}          2        0.33       57.70
{txt}       1165 {c |}{res}          2        0.33       58.03
{txt}       1167 {c |}{res}          1        0.16       58.20
{txt}       1168 {c |}{res}          2        0.33       58.52
{txt}       1169 {c |}{res}          1        0.16       58.69
{txt}       1171 {c |}{res}          3        0.49       59.18
{txt}       1174 {c |}{res}          1        0.16       59.34
{txt}       1175 {c |}{res}          2        0.33       59.67
{txt}       1180 {c |}{res}          1        0.16       59.84
{txt}       1181 {c |}{res}          1        0.16       60.00
{txt}       1186 {c |}{res}          1        0.16       60.16
{txt}       1187 {c |}{res}          2        0.33       60.49
{txt}       1190 {c |}{res}          1        0.16       60.66
{txt}       1193 {c |}{res}          1        0.16       60.82
{txt}       1195 {c |}{res}          1        0.16       60.98
{txt}       1200 {c |}{res}          1        0.16       61.15
{txt}       1204 {c |}{res}          1        0.16       61.31
{txt}       1210 {c |}{res}          1        0.16       61.48
{txt}       1211 {c |}{res}          1        0.16       61.64
{txt}       1215 {c |}{res}          1        0.16       61.80
{txt}       1219 {c |}{res}          1        0.16       61.97
{txt}       1227 {c |}{res}          1        0.16       62.13
{txt}       1237 {c |}{res}          1        0.16       62.30
{txt}       1239 {c |}{res}          2        0.33       62.62
{txt}       1241 {c |}{res}          1        0.16       62.79
{txt}       1242 {c |}{res}          1        0.16       62.95
{txt}       1261 {c |}{res}          1        0.16       63.11
{txt}       1262 {c |}{res}          1        0.16       63.28
{txt}       1264 {c |}{res}          4        0.66       63.93
{txt}       1265 {c |}{res}          2        0.33       64.26
{txt}       1266 {c |}{res}          1        0.16       64.43
{txt}       1267 {c |}{res}          1        0.16       64.59
{txt}       1268 {c |}{res}          1        0.16       64.75
{txt}       1275 {c |}{res}          1        0.16       64.92
{txt}       1276 {c |}{res}          1        0.16       65.08
{txt}       1283 {c |}{res}          1        0.16       65.25
{txt}       1290 {c |}{res}          1        0.16       65.41
{txt}       1291 {c |}{res}          2        0.33       65.74
{txt}       1294 {c |}{res}          1        0.16       65.90
{txt}       1295 {c |}{res}          2        0.33       66.23
{txt}       1297 {c |}{res}          1        0.16       66.39
{txt}       1299 {c |}{res}          1        0.16       66.56
{txt}       1303 {c |}{res}          2        0.33       66.89
{txt}       1304 {c |}{res}          1        0.16       67.05
{txt}       1305 {c |}{res}          1        0.16       67.21
{txt}       1309 {c |}{res}          2        0.33       67.54
{txt}       1314 {c |}{res}          1        0.16       67.70
{txt}       1321 {c |}{res}          1        0.16       67.87
{txt}       1325 {c |}{res}          1        0.16       68.03
{txt}       1327 {c |}{res}          1        0.16       68.20
{txt}       1328 {c |}{res}          2        0.33       68.52
{txt}       1329 {c |}{res}          1        0.16       68.69
{txt}       1333 {c |}{res}          1        0.16       68.85
{txt}       1334 {c |}{res}          2        0.33       69.18
{txt}       1339 {c |}{res}          1        0.16       69.34
{txt}       1342 {c |}{res}          1        0.16       69.51
{txt}       1346 {c |}{res}          1        0.16       69.67
{txt}       1357 {c |}{res}          1        0.16       69.84
{txt}       1358 {c |}{res}          1        0.16       70.00
{txt}       1359 {c |}{res}          3        0.49       70.49
{txt}       1360 {c |}{res}          1        0.16       70.66
{txt}       1361 {c |}{res}          1        0.16       70.82
{txt}       1362 {c |}{res}          2        0.33       71.15
{txt}       1363 {c |}{res}          1        0.16       71.31
{txt}       1367 {c |}{res}          1        0.16       71.48
{txt}       1377 {c |}{res}          1        0.16       71.64
{txt}       1381 {c |}{res}          1        0.16       71.80
{txt}       1383 {c |}{res}          1        0.16       71.97
{txt}       1384 {c |}{res}          1        0.16       72.13
{txt}       1390 {c |}{res}          1        0.16       72.30
{txt}       1392 {c |}{res}          1        0.16       72.46
{txt}       1395 {c |}{res}          1        0.16       72.62
{txt}       1397 {c |}{res}          1        0.16       72.79
{txt}       1398 {c |}{res}          1        0.16       72.95
{txt}       1400 {c |}{res}          1        0.16       73.11
{txt}       1403 {c |}{res}          1        0.16       73.28
{txt}       1404 {c |}{res}          2        0.33       73.61
{txt}       1405 {c |}{res}          2        0.33       73.93
{txt}       1407 {c |}{res}          1        0.16       74.10
{txt}       1409 {c |}{res}          1        0.16       74.26
{txt}       1410 {c |}{res}          1        0.16       74.43
{txt}       1413 {c |}{res}          1        0.16       74.59
{txt}       1415 {c |}{res}          2        0.33       74.92
{txt}       1419 {c |}{res}          1        0.16       75.08
{txt}       1420 {c |}{res}          1        0.16       75.25
{txt}       1421 {c |}{res}          1        0.16       75.41
{txt}       1424 {c |}{res}          1        0.16       75.57
{txt}       1426 {c |}{res}          1        0.16       75.74
{txt}       1435 {c |}{res}          2        0.33       76.07
{txt}       1436 {c |}{res}          3        0.49       76.56
{txt}       1438 {c |}{res}          1        0.16       76.72
{txt}       1439 {c |}{res}          1        0.16       76.89
{txt}       1441 {c |}{res}          1        0.16       77.05
{txt}       1442 {c |}{res}          3        0.49       77.54
{txt}       1443 {c |}{res}          3        0.49       78.03
{txt}       1444 {c |}{res}          2        0.33       78.36
{txt}       1446 {c |}{res}          1        0.16       78.52
{txt}       1448 {c |}{res}          1        0.16       78.69
{txt}       1449 {c |}{res}          2        0.33       79.02
{txt}       1450 {c |}{res}          1        0.16       79.18
{txt}       1451 {c |}{res}          1        0.16       79.34
{txt}       1452 {c |}{res}          1        0.16       79.51
{txt}       1453 {c |}{res}          1        0.16       79.67
{txt}       1455 {c |}{res}          2        0.33       80.00
{txt}       1456 {c |}{res}          1        0.16       80.16
{txt}       1457 {c |}{res}          3        0.49       80.66
{txt}       1458 {c |}{res}          4        0.66       81.31
{txt}       1459 {c |}{res}          2        0.33       81.64
{txt}       1460 {c |}{res}          2        0.33       81.97
{txt}       1461 {c |}{res}          5        0.82       82.79
{txt}       1463 {c |}{res}          2        0.33       83.11
{txt}       1466 {c |}{res}          1        0.16       83.28
{txt}       1468 {c |}{res}          1        0.16       83.44
{txt}       1472 {c |}{res}          2        0.33       83.77
{txt}       1473 {c |}{res}          2        0.33       84.10
{txt}       1474 {c |}{res}          2        0.33       84.43
{txt}       1479 {c |}{res}          1        0.16       84.59
{txt}       1480 {c |}{res}          1        0.16       84.75
{txt}       1483 {c |}{res}          1        0.16       84.92
{txt}       1485 {c |}{res}          1        0.16       85.08
{txt}       1486 {c |}{res}          1        0.16       85.25
{txt}       1487 {c |}{res}          1        0.16       85.41
{txt}       1494 {c |}{res}          1        0.16       85.57
{txt}       1500 {c |}{res}          2        0.33       85.90
{txt}       1513 {c |}{res}          1        0.16       86.07
{txt}       1514 {c |}{res}          1        0.16       86.23
{txt}       1533 {c |}{res}          1        0.16       86.39
{txt}       1547 {c |}{res}          1        0.16       86.56
{txt}       1549 {c |}{res}          1        0.16       86.72
{txt}       1556 {c |}{res}          2        0.33       87.05
{txt}       1557 {c |}{res}          1        0.16       87.21
{txt}       1560 {c |}{res}          1        0.16       87.38
{txt}       1572 {c |}{res}          1        0.16       87.54
{txt}       1593 {c |}{res}          1        0.16       87.70
{txt}       1601 {c |}{res}          1        0.16       87.87
{txt}       1614 {c |}{res}          2        0.33       88.20
{txt}       1622 {c |}{res}          1        0.16       88.36
{txt}       1624 {c |}{res}          1        0.16       88.52
{txt}       1625 {c |}{res}          1        0.16       88.69
{txt}       1632 {c |}{res}          2        0.33       89.02
{txt}       1636 {c |}{res}          1        0.16       89.18
{txt}       1646 {c |}{res}          1        0.16       89.34
{txt}       1655 {c |}{res}          1        0.16       89.51
{txt}       1692 {c |}{res}          1        0.16       89.67
{txt}       1701 {c |}{res}          1        0.16       89.84
{txt}       1718 {c |}{res}          1        0.16       90.00
{txt}       1746 {c |}{res}          1        0.16       90.16
{txt}       1768 {c |}{res}          1        0.16       90.33
{txt}       1801 {c |}{res}          1        0.16       90.49
{txt}       1819 {c |}{res}          1        0.16       90.66
{txt}       1825 {c |}{res}          2        0.33       90.98
{txt}       1827 {c |}{res}          1        0.16       91.15
{txt}       1848 {c |}{res}          1        0.16       91.31
{txt}       1857 {c |}{res}          1        0.16       91.48
{txt}       1873 {c |}{res}          1        0.16       91.64
{txt}       1874 {c |}{res}          2        0.33       91.97
{txt}       1885 {c |}{res}          1        0.16       92.13
{txt}       1897 {c |}{res}          1        0.16       92.30
{txt}       1929 {c |}{res}          1        0.16       92.46
{txt}       1942 {c |}{res}          1        0.16       92.62
{txt}       1974 {c |}{res}          1        0.16       92.79
{txt}       1989 {c |}{res}          1        0.16       92.95
{txt}       1995 {c |}{res}          1        0.16       93.11
{txt}       2078 {c |}{res}          1        0.16       93.28
{txt}       2115 {c |}{res}          1        0.16       93.44
{txt}       2157 {c |}{res}          1        0.16       93.61
{txt}       2180 {c |}{res}          1        0.16       93.77
{txt}       2185 {c |}{res}          1        0.16       93.93
{txt}       2192 {c |}{res}          1        0.16       94.10
{txt}       2230 {c |}{res}          1        0.16       94.26
{txt}       2250 {c |}{res}          1        0.16       94.43
{txt}       2255 {c |}{res}          1        0.16       94.59
{txt}       2304 {c |}{res}          1        0.16       94.75
{txt}       2318 {c |}{res}          1        0.16       94.92
{txt}       2340 {c |}{res}          1        0.16       95.08
{txt}       2353 {c |}{res}          1        0.16       95.25
{txt}       2357 {c |}{res}          1        0.16       95.41
{txt}       2374 {c |}{res}          1        0.16       95.57
{txt}       2379 {c |}{res}          1        0.16       95.74
{txt}       2454 {c |}{res}          1        0.16       95.90
{txt}       2497 {c |}{res}          1        0.16       96.07
{txt}       2517 {c |}{res}          1        0.16       96.23
{txt}       2523 {c |}{res}          2        0.33       96.56
{txt}       2557 {c |}{res}          1        0.16       96.72
{txt}       2627 {c |}{res}          1        0.16       96.89
{txt}       2651 {c |}{res}          1        0.16       97.05
{txt}       2762 {c |}{res}          1        0.16       97.21
{txt}       2782 {c |}{res}          1        0.16       97.38
{txt}       2794 {c |}{res}          2        0.33       97.70
{txt}       2810 {c |}{res}          1        0.16       97.87
{txt}       2846 {c |}{res}          1        0.16       98.03
{txt}       2871 {c |}{res}          1        0.16       98.20
{txt}       2907 {c |}{res}          1        0.16       98.36
{txt}       2911 {c |}{res}          1        0.16       98.52
{txt}       2919 {c |}{res}          1        0.16       98.69
{txt}       2920 {c |}{res}          3        0.49       99.18
{txt}       2921 {c |}{res}          1        0.16       99.34
{txt}       2992 {c |}{res}          1        0.16       99.51
{txt}       3118 {c |}{res}          1        0.16       99.67
{txt}       3229 {c |}{res}          1        0.16       99.84
{txt}       4074 {c |}{res}          1        0.16      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        610      100.00
{txt}
{com}. 
. *
. *
. *
. *
. 
. **** SHOW DIFFERENCES BETWEEN HIGH LOYALISTS WITH A MUTUAL POLICY COMMITTMENT [highloyalpp==1, n=236] AND HIGH LOYALISTS THAT DO NOT [highloyalpp==0, n=100]: where HIGH LOYALISTS ARE DEFINED AS "zloyalmedian>=0" [LIES BETWEEN MEDIAN = -0.1646416 & MEAN = 0.1284699: 61st Percentile] ***
. 
. sum zloyalmedian, detail

                        {txt}zloyalmedian
{hline 61}
      Percentiles      Smallest
 1%    {res}-1.706393      -1.844698
{txt} 5%    {res}-1.313641      -1.825194
{txt}10%    {res}-.6451644      -1.816328       {txt}Obs         {res}        860
{txt}25%    {res}-.3960373      -1.811008       {txt}Sum of Wgt. {res}        860

{txt}50%    {res}-.1646416                      {txt}Mean          {res} .1284699
                        {txt}Largest       Std. Dev.     {res} .9110985
{txt}75%    {res} .9692858       2.331063
{txt}90%    {res} 1.711348       2.409081       {txt}Variance      {res} .8301005
{txt}95%    {res} 1.925898       2.508377       {txt}Skewness      {res} .6447668
{txt}99%    {res} 2.185664       2.731794       {txt}Kurtosis      {res} 2.885473
{txt}
{com}. 
. 
. 
. *
. gen highloyalpp = 1 if zloyalmedian>=0 & soubinaryagency2nom==1
{txt}(637 missing values generated)

{com}. *
. replace highloyalpp = 0 if zloyalmedian>=0 & soubinaryagency2nom==0
{txt}(113 real changes made)

{com}. 
. 
. 
. 
. *********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** APPENDIX C SURVIVAL REGRESSION ANALYSES: COX SEMIPARAMETRIC & WEIBULL PARAMETRIC MODELS [INCLUSION OF POST-SELECTION/NOMINATION CHANGES TO AGENCY AGENDA STATUS INTERACTION WITH PRESIDENTIAL LOYALTY COVARIATE] ****
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** APPENDIX C SURVIVAL REGRESSION MODELS  ***
. 
. 
. ** SET FOR SURVIVAL DATA WITH A SINGLE RECORD PER APPOINTEE OBSERVATION [N = 860: UNCENSORED N = 831; CENSORED N = 29] ** 
. stset okapptdur, failure(singleadmin_service)

     {txt}failure event:  {res}singleadmin_service != 0 & singleadmin_service < .
{txt}obs. time interval:  {res}(0, okapptdur]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}        860{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}        860{txt}  observations remaining, representing
{res}        831{txt}  failures in single-record/single-failure data
{res}    850,034{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}    4,074
{txt}
{com}. 
. 
. 
. **** MODEL C1: COX MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom   c.zloyalmedian##i.soubinaryagency2onoff  c.zloyalmedian##i.soubinaryagency2offon   zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr  ,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}note: zloyalmedian omitted because of collinearity
note: zloyalmedian omitted because of collinearity
Iteration 0:   log pseudolikelihood = {res}-4793.4442
{txt}Iteration 1:   log pseudolikelihood = {res}-4530.3559
{txt}Iteration 2:   log pseudolikelihood = {res}-4512.5421
{txt}Iteration 3:   log pseudolikelihood = {res}-4512.3064
{txt}Iteration 4:   log pseudolikelihood = {res}-4512.3061
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-4512.3061

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}26{txt})    =  {res}   1406.65
{txt}Log pseudolikelihood =   {res}-4512.3061             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 102:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                                  _t{col 38}{c |} Haz. Ratio{col 50}   Std. Err.{col 62}      z{col 70}   P>|z|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}zloyalmedian {c |}{col 38}{res}{space 2} 1.489041{col 50}{space 2} .1934351{col 61}{space 1}    3.06{col 70}{space 3}0.002{col 78}{space 4} 1.154332{col 91}{space 3} 1.920802
{txt}{space 15}1.soubinaryagency2nom {c |}{col 38}{res}{space 2} 1.039587{col 50}{space 2} .1404935{col 61}{space 1}    0.29{col 70}{space 3}0.774{col 78}{space 4} .7976759{col 91}{space 3} 1.354863
{txt}{space 36} {c |}
{space 2}soubinaryagency2nom#c.zloyalmedian {c |}
{space 34}1  {c |}{col 38}{res}{space 2} .6171134{col 50}{space 2} .0873099{col 61}{space 1}   -3.41{col 70}{space 3}0.001{col 78}{space 4} .4676663{col 91}{space 3} .8143178
{txt}{space 36} {c |}
{space 24}zloyalmedian {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 13}1.soubinaryagency2onoff {c |}{col 38}{res}{space 2} 1.206098{col 50}{space 2} .1397585{col 61}{space 1}    1.62{col 70}{space 3}0.106{col 78}{space 4} .9610554{col 91}{space 3}  1.51362
{txt}{space 36} {c |}
soubinaryagency2onoff#c.zloyalmedian {c |}
{space 34}1  {c |}{col 38}{res}{space 2}  .900194{col 50}{space 2}  .094767{col 61}{space 1}   -1.00{col 70}{space 3}0.318{col 78}{space 4} .7323636{col 91}{space 3} 1.106485
{txt}{space 36} {c |}
{space 24}zloyalmedian {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 13}1.soubinaryagency2offon {c |}{col 38}{res}{space 2} .9522377{col 50}{space 2} .1943926{col 61}{space 1}   -0.24{col 70}{space 3}0.811{col 78}{space 4} .6382321{col 91}{space 3} 1.420732
{txt}{space 36} {c |}
soubinaryagency2offon#c.zloyalmedian {c |}
{space 34}1  {c |}{col 38}{res}{space 2} .8732873{col 50}{space 2} .1233702{col 61}{space 1}   -0.96{col 70}{space 3}0.338{col 78}{space 4} .6620747{col 91}{space 3}  1.15188
{txt}{space 36} {c |}
{space 23}zpecompmedian {c |}{col 38}{res}{space 2}  .988238{col 50}{space 2}  .068481{col 61}{space 1}   -0.17{col 70}{space 3}0.864{col 78}{space 4} .8627335{col 91}{space 3}    1.132
{txt}{space 23}zmecompmedian {c |}{col 38}{res}{space 2} 1.005551{col 50}{space 2} .0598253{col 61}{space 1}    0.09{col 70}{space 3}0.926{col 78}{space 4} .8948739{col 91}{space 3} 1.129917
{txt}{space 27}toplevel2 {c |}{col 38}{res}{space 2} .5815743{col 50}{space 2} .0456636{col 61}{space 1}   -6.90{col 70}{space 3}0.000{col 78}{space 4} .4986218{col 91}{space 3} .6783271
{txt}{space 16}presagencyideolalign {c |}{col 38}{res}{space 2}  1.44698{col 50}{space 2} .1366506{col 61}{space 1}    3.91{col 70}{space 3}0.000{col 78}{space 4} 1.202476{col 91}{space 3} 1.741201
{txt}{space 14}presagencyideolopposed {c |}{col 38}{res}{space 2} 1.368058{col 50}{space 2} .1438249{col 61}{space 1}    2.98{col 70}{space 3}0.003{col 78}{space 4} 1.113313{col 91}{space 3} 1.681094
{txt}{space 21}subagencydesign {c |}{col 38}{res}{space 2} 1.075469{col 50}{space 2} .1639346{col 61}{space 1}    0.48{col 70}{space 3}0.633{col 78}{space 4}  .797716{col 91}{space 3}  1.44993
{txt}{space 14}standaloneagencydesign {c |}{col 38}{res}{space 2}  .810456{col 50}{space 2} .0763208{col 61}{space 1}   -2.23{col 70}{space 3}0.026{col 78}{space 4} .6738631{col 91}{space 3} .9747365
{txt}{space 10}okstartsenpolarizationmean {c |}{col 38}{res}{space 2} .0004648{col 50}{space 2} .0012172{col 61}{space 1}   -2.93{col 70}{space 3}0.003{col 78}{space 4} 2.74e-06{col 91}{space 3} .0787874
{txt}{space 13}okstartfilipresdistance {c |}{col 38}{res}{space 2} 1.757038{col 50}{space 2} .3802238{col 61}{space 1}    2.60{col 70}{space 3}0.009{col 78}{space 4}  1.14969{col 91}{space 3}  2.68523
{txt}{space 25}okcrossover {c |}{col 38}{res}{space 2}  .192773{col 50}{space 2} .0338715{col 61}{space 1}   -9.37{col 70}{space 3}0.000{col 78}{space 4} .1366106{col 91}{space 3} .2720244
{txt}{space 22}okstartpresapp {c |}{col 38}{res}{space 2} .9945058{col 50}{space 2} .0034199{col 61}{space 1}   -1.60{col 70}{space 3}0.109{col 78}{space 4} .9878254{col 91}{space 3} 1.001231
{txt}{space 17}okstartunemployment {c |}{col 38}{res}{space 2} .9396192{col 50}{space 2} .0441054{col 61}{space 1}   -1.33{col 70}{space 3}0.185{col 78}{space 4} .8570315{col 91}{space 3} 1.030165
{txt}{space 36} {c |}
{space 25}okstartadyr {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.892796{col 50}{space 2} .3719931{col 61}{space 1}    3.25{col 70}{space 3}0.001{col 78}{space 4} 1.287705{col 91}{space 3} 2.782219
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 5.341061{col 50}{space 2} .7522656{col 61}{space 1}   11.90{col 70}{space 3}0.000{col 78}{space 4} 4.052653{col 91}{space 3} 7.039075
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  4.36451{col 50}{space 2}  1.43816{col 61}{space 1}    4.47{col 70}{space 3}0.000{col 78}{space 4}  2.28799{col 91}{space 3} 8.325627
{txt}{space 34}5  {c |}{col 38}{res}{space 2} 1.367973{col 50}{space 2} .1714896{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.069968{col 91}{space 3} 1.748979
{txt}{space 34}6  {c |}{col 38}{res}{space 2} 2.841924{col 50}{space 2} .3865517{col 61}{space 1}    7.68{col 70}{space 3}0.000{col 78}{space 4} 2.176877{col 91}{space 3} 3.710144
{txt}{space 34}7  {c |}{col 38}{res}{space 2} 5.304022{col 50}{space 2} 1.360169{col 61}{space 1}    6.51{col 70}{space 3}0.000{col 78}{space 4} 3.208649{col 91}{space 3} 8.767755
{txt}{space 34}8  {c |}{col 38}{res}{space 2} 7.685137{col 50}{space 2} 2.321634{col 61}{space 1}    6.75{col 70}{space 3}0.000{col 78}{space 4} 4.251172{col 91}{space 3} 13.89295
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-4793.444{col 39}-4512.306{col 50}    26{col 58} 9076.612{col 69} 9200.293
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelC1
{txt}
{com}. 
. 
. *** COMPUTE Figure C2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [CM1−CM4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. 
. 
. 
. ** ONE INTERQUARTILE RANGE MARGINAL EFFECT INCREASE IN APPOINTEE LOYALTY DIFFERENTIAL BETWEEN POLICY PRIORITY AGENCY VERSUS NON-POLICY PRIORITY AGENCY [FIGURE 2] **
. 
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5173453{col 26}{space 2} .0999343{col 37}{space 1}   -3.41{col 46}{space 3}0.001{col 54}{space 4} .3542877{col 67}{space 3} .7554486
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC1zloyalnom = r(table)
{txt}
{com}. mat list modelC1zloyalnom
{res}
{txt}modelC1zloyalnom[9,1]
               (1)
     b {res}   .5173453
{txt}    se {res}  .09993426
{txt}     z {res} -3.4117798
{txt}pvalue {res}   .0006454
{txt}    ll {res}  .35428771
{txt}    ul {res}   .7554486
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. *
. 
. estimates restore modelC1
{txt}(results {stata estimates replay modelC1:modelC1} are active now)

{com}. 
. lincomest 1.soubinaryagency2onoff#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2onoff#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .8662715{col 26}{space 2} .1245118{col 37}{space 1}   -1.00{col 46}{space 3}0.318{col 54}{space 4} .6535944{col 67}{space 3} 1.148153
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC1zloyalonoff = r(table)
{txt}
{com}. mat list modelC1zloyalonoff
{res}
{txt}modelC1zloyalonoff[9,1]
              (1)
     b {res} .86627147
{txt}    se {res} .12451177
{txt}     z {res} -.9987753
{txt}pvalue {res} .31790355
{txt}    ll {res} .65359435
{txt}    ul {res} 1.1481529
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         1
{reset}
{com}. *
. 
. estimates restore modelC1
{txt}(results {stata estimates replay modelC1:modelC1} are active now)

{com}. 
. lincomest 1.soubinaryagency2offon#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2offon#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .8311137{col 26}{space 2} .1603057{col 37}{space 1}   -0.96{col 46}{space 3}0.338{col 54}{space 4} .5694822{col 67}{space 3} 1.212944
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC1zloyaloffon = r(table)
{txt}
{com}. mat list modelC1zloyaloffon
{res}
{txt}modelC1zloyaloffon[9,1]
               (1)
     b {res}  .83111374
{txt}    se {res}  .16030575
{txt}     z {res} -.95908342
{txt}pvalue {res}  .33751672
{txt}    ll {res}  .56948222
{txt}    ul {res}   1.212944
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. 
. 
. 
. **** MODEL C2: COX MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom   c.zloyalmedian##i.soubinaryagency2onoff  c.zloyalmedian##i.soubinaryagency2offon   zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr  i.sbagency reagan bush41 clinton bush43,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}note: zloyalmedian omitted because of collinearity
note: zloyalmedian omitted because of collinearity
note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
Iteration 0:   log pseudolikelihood = {res}-4793.4442
{txt}Iteration 1:   log pseudolikelihood = {res}-4497.1523
{txt}Iteration 2:   log pseudolikelihood = {res}-4470.1476
{txt}Iteration 3:   log pseudolikelihood = {res}-4469.7959
{txt}Iteration 4:   log pseudolikelihood = {res}-4469.7956
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-4469.7956

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}40{txt})    =  {res}  33911.51
{txt}Log pseudolikelihood =   {res}-4469.7956             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 102:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                                  _t{col 38}{c |} Haz. Ratio{col 50}   Std. Err.{col 62}      z{col 70}   P>|z|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}zloyalmedian {c |}{col 38}{res}{space 2} 1.404297{col 50}{space 2} .2266594{col 61}{space 1}    2.10{col 70}{space 3}0.035{col 78}{space 4} 1.023462{col 91}{space 3} 1.926844
{txt}{space 15}1.soubinaryagency2nom {c |}{col 38}{res}{space 2} 1.045407{col 50}{space 2} .2437713{col 61}{space 1}    0.19{col 70}{space 3}0.849{col 78}{space 4} .6619114{col 91}{space 3} 1.651092
{txt}{space 36} {c |}
{space 2}soubinaryagency2nom#c.zloyalmedian {c |}
{space 34}1  {c |}{col 38}{res}{space 2} .6269512{col 50}{space 2} .1172338{col 61}{space 1}   -2.50{col 70}{space 3}0.013{col 78}{space 4} .4345775{col 91}{space 3} .9044826
{txt}{space 36} {c |}
{space 24}zloyalmedian {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 13}1.soubinaryagency2onoff {c |}{col 38}{res}{space 2} 1.121247{col 50}{space 2} .1711205{col 61}{space 1}    0.75{col 70}{space 3}0.453{col 78}{space 4} .8313693{col 91}{space 3} 1.512197
{txt}{space 36} {c |}
soubinaryagency2onoff#c.zloyalmedian {c |}
{space 34}1  {c |}{col 38}{res}{space 2} .8558932{col 50}{space 2} .1243356{col 61}{space 1}   -1.07{col 70}{space 3}0.284{col 78}{space 4} .6438216{col 91}{space 3}  1.13782
{txt}{space 36} {c |}
{space 24}zloyalmedian {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 13}1.soubinaryagency2offon {c |}{col 38}{res}{space 2} .8896292{col 50}{space 2} .2284431{col 61}{space 1}   -0.46{col 70}{space 3}0.649{col 78}{space 4} .5378158{col 91}{space 3} 1.471582
{txt}{space 36} {c |}
soubinaryagency2offon#c.zloyalmedian {c |}
{space 34}1  {c |}{col 38}{res}{space 2} .9833993{col 50}{space 2}  .172543{col 61}{space 1}   -0.10{col 70}{space 3}0.924{col 78}{space 4}  .697239{col 91}{space 3} 1.387005
{txt}{space 36} {c |}
{space 23}zpecompmedian {c |}{col 38}{res}{space 2} 1.036276{col 50}{space 2} .0805726{col 61}{space 1}    0.46{col 70}{space 3}0.647{col 78}{space 4}  .889801{col 91}{space 3} 1.206864
{txt}{space 23}zmecompmedian {c |}{col 38}{res}{space 2} .9774445{col 50}{space 2} .0667778{col 61}{space 1}   -0.33{col 70}{space 3}0.738{col 78}{space 4} .8549468{col 91}{space 3} 1.117494
{txt}{space 27}toplevel2 {c |}{col 38}{res}{space 2} .5085464{col 50}{space 2} .0540156{col 61}{space 1}   -6.37{col 70}{space 3}0.000{col 78}{space 4} .4129711{col 91}{space 3} .6262411
{txt}{space 16}presagencyideolalign {c |}{col 38}{res}{space 2} .6453456{col 50}{space 2} .1709926{col 61}{space 1}   -1.65{col 70}{space 3}0.098{col 78}{space 4} .3839332{col 91}{space 3} 1.084749
{txt}{space 14}presagencyideolopposed {c |}{col 38}{res}{space 2} .6225591{col 50}{space 2} .1693599{col 61}{space 1}   -1.74{col 70}{space 3}0.081{col 78}{space 4} .3652761{col 91}{space 3}  1.06106
{txt}{space 21}subagencydesign {c |}{col 38}{res}{space 2} 1.667522{col 50}{space 2} .3608214{col 61}{space 1}    2.36{col 70}{space 3}0.018{col 78}{space 4} 1.091156{col 91}{space 3} 2.548332
{txt}{space 14}standaloneagencydesign {c |}{col 38}{res}{space 2} 2.065046{col 50}{space 2} .6066653{col 61}{space 1}    2.47{col 70}{space 3}0.014{col 78}{space 4} 1.161089{col 91}{space 3} 3.672773
{txt}{space 10}okstartsenpolarizationmean {c |}{col 38}{res}{space 2} 9.21e-12{col 50}{space 2} 9.57e-11{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} 1.29e-20{col 91}{space 3} .0065608
{txt}{space 13}okstartfilipresdistance {c |}{col 38}{res}{space 2} 1246.952{col 50}{space 2} 2815.741{col 61}{space 1}    3.16{col 70}{space 3}0.002{col 78}{space 4} 14.91947{col 91}{space 3} 104218.9
{txt}{space 25}okcrossover {c |}{col 38}{res}{space 2} .1637767{col 50}{space 2} .0360372{col 61}{space 1}   -8.22{col 70}{space 3}0.000{col 78}{space 4} .1064033{col 91}{space 3} .2520864
{txt}{space 22}okstartpresapp {c |}{col 38}{res}{space 2} .9896065{col 50}{space 2} .0045521{col 61}{space 1}   -2.27{col 70}{space 3}0.023{col 78}{space 4} .9807246{col 91}{space 3} .9985688
{txt}{space 17}okstartunemployment {c |}{col 38}{res}{space 2} 1.147108{col 50}{space 2} .0987708{col 61}{space 1}    1.59{col 70}{space 3}0.111{col 78}{space 4} .9689747{col 91}{space 3} 1.357989
{txt}{space 36} {c |}
{space 25}okstartadyr {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.611804{col 50}{space 2} .3702547{col 61}{space 1}    2.08{col 70}{space 3}0.038{col 78}{space 4} 1.027493{col 91}{space 3} 2.528398
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 3.949609{col 50}{space 2} .8791274{col 61}{space 1}    6.17{col 70}{space 3}0.000{col 78}{space 4} 2.553222{col 91}{space 3} 6.109696
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 3.550625{col 50}{space 2} 1.196908{col 61}{space 1}    3.76{col 70}{space 3}0.000{col 78}{space 4} 1.833862{col 91}{space 3} 6.874529
{txt}{space 34}5  {c |}{col 38}{res}{space 2} 1.663555{col 50}{space 2} .4146856{col 61}{space 1}    2.04{col 70}{space 3}0.041{col 78}{space 4} 1.020592{col 91}{space 3} 2.711577
{txt}{space 34}6  {c |}{col 38}{res}{space 2} 3.762543{col 50}{space 2} .9657419{col 61}{space 1}    5.16{col 70}{space 3}0.000{col 78}{space 4} 2.275104{col 91}{space 3} 6.222453
{txt}{space 34}7  {c |}{col 38}{res}{space 2}   5.7418{col 50}{space 2}  1.78105{col 61}{space 1}    5.63{col 70}{space 3}0.000{col 78}{space 4} 3.126178{col 91}{space 3} 10.54587
{txt}{space 34}8  {c |}{col 38}{res}{space 2} 9.187562{col 50}{space 2}  3.48728{col 61}{space 1}    5.84{col 70}{space 3}0.000{col 78}{space 4} 4.366301{col 91}{space 3} 19.33245
{txt}{space 36} {c |}
{space 28}sbagency {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 3.327428{col 50}{space 2} .9860675{col 61}{space 1}    4.06{col 70}{space 3}0.000{col 78}{space 4} 1.861484{col 91}{space 3} 5.947826
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.153574{col 50}{space 2} .6004447{col 61}{space 1}    2.75{col 70}{space 3}0.006{col 78}{space 4} 1.246905{col 91}{space 3} 3.719512
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.465717{col 50}{space 2} .3503596{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9174489{col 91}{space 3}  2.34163
{txt}{space 34}5  {c |}{col 38}{res}{space 2} 1.163086{col 50}{space 2} .3312171{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .6655966{col 91}{space 3} 2.032414
{txt}{space 34}6  {c |}{col 38}{res}{space 2} 3.165363{col 50}{space 2} .8475044{col 61}{space 1}    4.30{col 70}{space 3}0.000{col 78}{space 4} 1.872924{col 91}{space 3} 5.349669
{txt}{space 34}7  {c |}{col 38}{res}{space 2} 2.065193{col 50}{space 2} .6584033{col 61}{space 1}    2.27{col 70}{space 3}0.023{col 78}{space 4} 1.105578{col 91}{space 3}  3.85773
{txt}{space 34}8  {c |}{col 38}{res}{space 2} 2.734306{col 50}{space 2} .7729052{col 61}{space 1}    3.56{col 70}{space 3}0.000{col 78}{space 4} 1.571225{col 91}{space 3} 4.758345
{txt}{space 34}9  {c |}{col 38}{res}{space 2} 2.515332{col 50}{space 2} .7049525{col 61}{space 1}    3.29{col 70}{space 3}0.001{col 78}{space 4} 1.452231{col 91}{space 3} 4.356673
{txt}{space 33}11  {c |}{col 38}{res}{space 2} 4.542593{col 50}{space 2} 1.487914{col 61}{space 1}    4.62{col 70}{space 3}0.000{col 78}{space 4} 2.390535{col 91}{space 3} 8.632022
{txt}{space 33}12  {c |}{col 38}{res}{space 2} 1.798542{col 50}{space 2} .3448167{col 61}{space 1}    3.06{col 70}{space 3}0.002{col 78}{space 4} 1.235174{col 91}{space 3} 2.618864
{txt}{space 33}13  {c |}{col 38}{res}{space 2} 1.719393{col 50}{space 2} .4381449{col 61}{space 1}    2.13{col 70}{space 3}0.033{col 78}{space 4}  1.04344{col 91}{space 3} 2.833237
{txt}{space 33}14  {c |}{col 38}{res}{space 2} 2.912529{col 50}{space 2} .8579584{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.635037{col 91}{space 3} 5.188156
{txt}{space 33}15  {c |}{col 38}{res}{space 2}  1.83945{col 50}{space 2}  .525306{col 61}{space 1}    2.13{col 70}{space 3}0.033{col 78}{space 4} 1.051003{col 91}{space 3}  3.21938
{txt}{space 33}16  {c |}{col 38}{res}{space 2} .8974246{col 50}{space 2} .1483924{col 61}{space 1}   -0.65{col 70}{space 3}0.513{col 78}{space 4}  .649006{col 91}{space 3}  1.24093
{txt}{space 33}17  {c |}{col 38}{res}{space 2} 1.675601{col 50}{space 2} .1376299{col 61}{space 1}    6.28{col 70}{space 3}0.000{col 78}{space 4} 1.426445{col 91}{space 3} 1.968277
{txt}{space 33}18  {c |}{col 38}{res}{space 2} 2.289789{col 50}{space 2} .7132483{col 61}{space 1}    2.66{col 70}{space 3}0.008{col 78}{space 4} 1.243523{col 91}{space 3} 4.216353
{txt}{space 33}19  {c |}{col 38}{res}{space 2} .7926895{col 50}{space 2} .1168092{col 61}{space 1}   -1.58{col 70}{space 3}0.115{col 78}{space 4}  .593843{col 91}{space 3} 1.058119
{txt}{space 33}20  {c |}{col 38}{res}{space 2} .2592346{col 50}{space 2} .0871851{col 61}{space 1}   -4.01{col 70}{space 3}0.000{col 78}{space 4}  .134097{col 91}{space 3} .5011489
{txt}{space 33}21  {c |}{col 38}{res}{space 2} .8053441{col 50}{space 2} .0870787{col 61}{space 1}   -2.00{col 70}{space 3}0.045{col 78}{space 4}  .651545{col 91}{space 3} .9954479
{txt}{space 33}22  {c |}{col 38}{res}{space 2} .4394323{col 50}{space 2} .1606294{col 61}{space 1}   -2.25{col 70}{space 3}0.024{col 78}{space 4}  .214657{col 91}{space 3}  .899578
{txt}{space 33}23  {c |}{col 38}{res}{space 2} .9652017{col 50}{space 2} .2549177{col 61}{space 1}   -0.13{col 70}{space 3}0.893{col 78}{space 4} .5751866{col 91}{space 3} 1.619673
{txt}{space 33}24  {c |}{col 38}{res}{space 2} .2850236{col 50}{space 2} .1453927{col 61}{space 1}   -2.46{col 70}{space 3}0.014{col 78}{space 4} .1048761{col 91}{space 3} .7746137
{txt}{space 33}25  {c |}{col 38}{res}{space 2}  1.38611{col 50}{space 2} .2159648{col 61}{space 1}    2.10{col 70}{space 3}0.036{col 78}{space 4} 1.021351{col 91}{space 3} 1.881137
{txt}{space 33}26  {c |}{col 38}{res}{space 2} .8069099{col 50}{space 2} .1317376{col 61}{space 1}   -1.31{col 70}{space 3}0.189{col 78}{space 4} .5859442{col 91}{space 3} 1.111204
{txt}{space 33}27  {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 33}28  {c |}{col 38}{res}{space 2} 1.462268{col 50}{space 2} .2384552{col 61}{space 1}    2.33{col 70}{space 3}0.020{col 78}{space 4} 1.062233{col 91}{space 3} 2.012956
{txt}{space 33}29  {c |}{col 38}{res}{space 2} 4.079749{col 50}{space 2} 1.468858{col 61}{space 1}    3.91{col 70}{space 3}0.000{col 78}{space 4} 2.014512{col 91}{space 3} 8.262225
{txt}{space 33}30  {c |}{col 38}{res}{space 2} 1.701078{col 50}{space 2} .5266643{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9272293{col 91}{space 3} 3.120768
{txt}{space 33}50  {c |}{col 38}{res}{space 2}   2.1749{col 50}{space 2} .4646396{col 61}{space 1}    3.64{col 70}{space 3}0.000{col 78}{space 4}  1.43084{col 91}{space 3} 3.305884
{txt}{space 33}51  {c |}{col 38}{res}{space 2} 3.593827{col 50}{space 2} 1.027539{col 61}{space 1}    4.47{col 70}{space 3}0.000{col 78}{space 4}  2.05203{col 91}{space 3} 6.294058
{txt}{space 33}52  {c |}{col 38}{res}{space 2} 1.524879{col 50}{space 2} .5440127{col 61}{space 1}    1.18{col 70}{space 3}0.237{col 78}{space 4} .7578133{col 91}{space 3} 3.068374
{txt}{space 33}53  {c |}{col 38}{res}{space 2} 1.517831{col 50}{space 2} .1707457{col 61}{space 1}    3.71{col 70}{space 3}0.000{col 78}{space 4}   1.2175{col 91}{space 3} 1.892247
{txt}{space 33}54  {c |}{col 38}{res}{space 2} 1.849965{col 50}{space 2} .3839433{col 61}{space 1}    2.96{col 70}{space 3}0.003{col 78}{space 4} 1.231698{col 91}{space 3} 2.778578
{txt}{space 33}55  {c |}{col 38}{res}{space 2} 1.419063{col 50}{space 2} .5439445{col 61}{space 1}    0.91{col 70}{space 3}0.361{col 78}{space 4} .6694615{col 91}{space 3}    3.008
{txt}{space 33}56  {c |}{col 38}{res}{space 2} 1.156866{col 50}{space 2} .4872269{col 61}{space 1}    0.35{col 70}{space 3}0.729{col 78}{space 4}  .506746{col 91}{space 3} 2.641046
{txt}{space 33}57  {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 33}58  {c |}{col 38}{res}{space 2} 1.526278{col 50}{space 2} .5672232{col 61}{space 1}    1.14{col 70}{space 3}0.255{col 78}{space 4} .7367067{col 91}{space 3} 3.162079
{txt}{space 33}59  {c |}{col 38}{res}{space 2} .3605143{col 50}{space 2} .1416009{col 61}{space 1}   -2.60{col 70}{space 3}0.009{col 78}{space 4} .1669522{col 91}{space 3} .7784897
{txt}{space 33}60  {c |}{col 38}{res}{space 2} 1.120698{col 50}{space 2} .1785521{col 61}{space 1}    0.72{col 70}{space 3}0.474{col 78}{space 4} .8201122{col 91}{space 3} 1.531454
{txt}{space 33}61  {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 36} {c |}
{space 30}reagan {c |}{col 38}{res}{space 2} .0548547{col 50}{space 2} .0522002{col 61}{space 1}   -3.05{col 70}{space 3}0.002{col 78}{space 4} .0084958{col 91}{space 3} .3541799
{txt}{space 30}bush41 {c |}{col 38}{res}{space 2}  .150659{col 50}{space 2} .0927511{col 61}{space 1}   -3.07{col 70}{space 3}0.002{col 78}{space 4}  .045078{col 91}{space 3} .5035298
{txt}{space 29}clinton {c |}{col 38}{res}{space 2} .6606512{col 50}{space 2} .3389028{col 61}{space 1}   -0.81{col 70}{space 3}0.419{col 78}{space 4} .2417243{col 91}{space 3} 1.805611
{txt}{space 30}bush43 {c |}{col 38}{res}{space 2} .2155592{col 50}{space 2}  .156195{col 61}{space 1}   -2.12{col 70}{space 3}0.034{col 78}{space 4} .0520934{col 91}{space 3}   .89197
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-4793.444{col 39}-4469.796{col 50}    40{col 58} 9019.591{col 69} 9209.868
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelC2
{txt}
{com}. 
. 
. *** COMPUTE Figure C2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [CM1−CM4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5286382{col 26}{space 2} .1349625{col 37}{space 1}   -2.50{col 46}{space 3}0.013{col 54}{space 4} .3205124{col 67}{space 3} .8719111
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC2zloyalnom = r(table)
{txt}
{com}. mat list modelC2zloyalnom
{res}
{txt}modelC2zloyalnom[9,1]
               (1)
     b {res}   .5286382
{txt}    se {res}  .13496251
{txt}     z {res} -2.4968487
{txt}pvalue {res}  .01253024
{txt}    ll {res}  .32051245
{txt}    ul {res}  .87191106
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. *
. 
. estimates restore modelC2
{txt}(results {stata estimates replay modelC2:modelC2} are active now)

{com}. 
. lincomest 1.soubinaryagency2onoff#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2onoff#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .8085947{col 26}{space 2} .1603771{col 37}{space 1}   -1.07{col 46}{space 3}0.284{col 54}{space 4} .5481547{col 67}{space 3} 1.192775
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC2zloyalonoff = r(table)
{txt}
{com}. mat list modelC2zloyalonoff
{res}
{txt}modelC2zloyalonoff[9,1]
               (1)
     b {res}  .80859471
{txt}    se {res}  .16037709
{txt}     z {res} -1.0711754
{txt}pvalue {res}  .28409059
{txt}    ll {res}  .54815471
{txt}    ul {res}  1.1927753
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. *
. 
. estimates restore modelC1
{txt}(results {stata estimates replay modelC1:modelC1} are active now)

{com}. 
. lincomest 1.soubinaryagency2offon#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2offon#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .8311137{col 26}{space 2} .1603057{col 37}{space 1}   -0.96{col 46}{space 3}0.338{col 54}{space 4} .5694822{col 67}{space 3} 1.212944
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC2zloyaloffon = r(table)
{txt}
{com}. mat list modelC2zloyaloffon
{res}
{txt}modelC2zloyaloffon[9,1]
               (1)
     b {res}  .83111374
{txt}    se {res}  .16030575
{txt}     z {res} -.95908342
{txt}pvalue {res}  .33751672
{txt}    ll {res}  .56948222
{txt}    ul {res}   1.212944
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. 
. 
. *******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** MODEL C3: WEIBULL MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom   c.zloyalmedian##i.soubinaryagency2onoff  c.zloyalmedian##i.soubinaryagency2offon    zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr,   distribution(weibull)  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: zloyalmedian omitted because of collinearity
note: zloyalmedian omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:-621.99201}  
Iteration 2:{space 3}log pseudolikelihood = {res:-539.50361}  
Iteration 3:{space 3}log pseudolikelihood = {res:-538.39648}  
Iteration 4:{space 3}log pseudolikelihood = {res:-538.39525}  
Iteration 5:{space 3}log pseudolikelihood = {res:-538.39525}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}26{txt})    =  {res}   1320.43
{txt}Log pseudolikelihood =   {res}-538.39525             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 102:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                                  _t{col 38}{c |} Haz. Ratio{col 50}   Std. Err.{col 62}      z{col 70}   P>|z|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}zloyalmedian {c |}{col 38}{res}{space 2} 1.527107{col 50}{space 2} .2159679{col 61}{space 1}    2.99{col 70}{space 3}0.003{col 78}{space 4} 1.157417{col 91}{space 3} 2.014878
{txt}{space 15}1.soubinaryagency2nom {c |}{col 38}{res}{space 2} 1.071418{col 50}{space 2} .1560699{col 61}{space 1}    0.47{col 70}{space 3}0.636{col 78}{space 4} .8053173{col 91}{space 3} 1.425445
{txt}{space 36} {c |}
{space 2}soubinaryagency2nom#c.zloyalmedian {c |}
{space 34}1  {c |}{col 38}{res}{space 2} .6023931{col 50}{space 2} .0913096{col 61}{space 1}   -3.34{col 70}{space 3}0.001{col 78}{space 4} .4475654{col 91}{space 3} .8107808
{txt}{space 36} {c |}
{space 24}zloyalmedian {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 13}1.soubinaryagency2onoff {c |}{col 38}{res}{space 2} 1.211724{col 50}{space 2} .1456863{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4}  .957334{col 91}{space 3} 1.533714
{txt}{space 36} {c |}
soubinaryagency2onoff#c.zloyalmedian {c |}
{space 34}1  {c |}{col 38}{res}{space 2} .9062994{col 50}{space 2} .0956976{col 61}{space 1}   -0.93{col 70}{space 3}0.351{col 78}{space 4} .7368719{col 91}{space 3} 1.114683
{txt}{space 36} {c |}
{space 24}zloyalmedian {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 13}1.soubinaryagency2offon {c |}{col 38}{res}{space 2} .9693603{col 50}{space 2} .2034046{col 61}{space 1}   -0.15{col 70}{space 3}0.882{col 78}{space 4} .6425017{col 91}{space 3} 1.462501
{txt}{space 36} {c |}
soubinaryagency2offon#c.zloyalmedian {c |}
{space 34}1  {c |}{col 38}{res}{space 2} .8533811{col 50}{space 2} .1347378{col 61}{space 1}   -1.00{col 70}{space 3}0.315{col 78}{space 4} .6262523{col 91}{space 3} 1.162885
{txt}{space 36} {c |}
{space 23}zpecompmedian {c |}{col 38}{res}{space 2} .9964087{col 50}{space 2} .0705066{col 61}{space 1}   -0.05{col 70}{space 3}0.959{col 78}{space 4}  .867373{col 91}{space 3} 1.144641
{txt}{space 23}zmecompmedian {c |}{col 38}{res}{space 2} 1.006489{col 50}{space 2} .0589626{col 61}{space 1}    0.11{col 70}{space 3}0.912{col 78}{space 4} .8973123{col 91}{space 3} 1.128949
{txt}{space 27}toplevel2 {c |}{col 38}{res}{space 2} .5992792{col 50}{space 2} .0479145{col 61}{space 1}   -6.40{col 70}{space 3}0.000{col 78}{space 4}  .512357{col 91}{space 3} .7009479
{txt}{space 16}presagencyideolalign {c |}{col 38}{res}{space 2}  1.46751{col 50}{space 2} .1409356{col 61}{space 1}    3.99{col 70}{space 3}0.000{col 78}{space 4} 1.215722{col 91}{space 3} 1.771447
{txt}{space 14}presagencyideolopposed {c |}{col 38}{res}{space 2} 1.374242{col 50}{space 2} .1448258{col 61}{space 1}    3.02{col 70}{space 3}0.003{col 78}{space 4} 1.117786{col 91}{space 3} 1.689538
{txt}{space 21}subagencydesign {c |}{col 38}{res}{space 2} 1.078083{col 50}{space 2} .1679761{col 61}{space 1}    0.48{col 70}{space 3}0.629{col 78}{space 4} .7943767{col 91}{space 3} 1.463113
{txt}{space 14}standaloneagencydesign {c |}{col 38}{res}{space 2} .8012574{col 50}{space 2} .0738488{col 61}{space 1}   -2.40{col 70}{space 3}0.016{col 78}{space 4} .6688366{col 91}{space 3} .9598957
{txt}{space 10}okstartsenpolarizationmean {c |}{col 38}{res}{space 2} .0004655{col 50}{space 2} .0012037{col 61}{space 1}   -2.97{col 70}{space 3}0.003{col 78}{space 4} 2.93e-06{col 91}{space 3} .0739714
{txt}{space 13}okstartfilipresdistance {c |}{col 38}{res}{space 2} 1.925425{col 50}{space 2} .4086193{col 61}{space 1}    3.09{col 70}{space 3}0.002{col 78}{space 4} 1.270229{col 91}{space 3} 2.918576
{txt}{space 25}okcrossover {c |}{col 38}{res}{space 2} .1986038{col 50}{space 2} .0357571{col 61}{space 1}   -8.98{col 70}{space 3}0.000{col 78}{space 4} .1395517{col 91}{space 3} .2826442
{txt}{space 22}okstartpresapp {c |}{col 38}{res}{space 2} .9951923{col 50}{space 2} .0034867{col 61}{space 1}   -1.38{col 70}{space 3}0.169{col 78}{space 4} .9883819{col 91}{space 3}  1.00205
{txt}{space 17}okstartunemployment {c |}{col 38}{res}{space 2} .9393889{col 50}{space 2} .0431119{col 61}{space 1}   -1.36{col 70}{space 3}0.173{col 78}{space 4}   .85858{col 91}{space 3} 1.027804
{txt}{space 36} {c |}
{space 25}okstartadyr {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.852487{col 50}{space 2} .3726307{col 61}{space 1}    3.06{col 70}{space 3}0.002{col 78}{space 4}  1.24892{col 91}{space 3}  2.74774
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 5.852259{col 50}{space 2} .7982627{col 61}{space 1}   12.95{col 70}{space 3}0.000{col 78}{space 4} 4.479376{col 91}{space 3} 7.645916
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 4.898927{col 50}{space 2} 1.495417{col 61}{space 1}    5.21{col 70}{space 3}0.000{col 78}{space 4} 2.693198{col 91}{space 3} 8.911147
{txt}{space 34}5  {c |}{col 38}{res}{space 2} 1.350267{col 50}{space 2} .1797599{col 61}{space 1}    2.26{col 70}{space 3}0.024{col 78}{space 4} 1.040159{col 91}{space 3} 1.752828
{txt}{space 34}6  {c |}{col 38}{res}{space 2}  2.78022{col 50}{space 2} .3435877{col 61}{space 1}    8.27{col 70}{space 3}0.000{col 78}{space 4} 2.182153{col 91}{space 3} 3.542201
{txt}{space 34}7  {c |}{col 38}{res}{space 2}  5.92597{col 50}{space 2} 1.409356{col 61}{space 1}    7.48{col 70}{space 3}0.000{col 78}{space 4} 3.718097{col 91}{space 3} 9.444918
{txt}{space 34}8  {c |}{col 38}{res}{space 2} 8.902593{col 50}{space 2} 2.590367{col 61}{space 1}    7.51{col 70}{space 3}0.000{col 78}{space 4} 5.033202{col 91}{space 3} 15.74667
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 3.13e-06{col 50}{space 2} 6.04e-06{col 61}{space 1}   -6.57{col 70}{space 3}0.000{col 78}{space 4} 7.15e-08{col 91}{space 3} .0001373
{txt}{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 31}/ln_p {c |}{col 38}{res}{space 2} .9192083{col 50}{space 2} .0312972{col 61}{space 1}   29.37{col 70}{space 3}0.000{col 78}{space 4} .8578669{col 91}{space 3} .9805497
{txt}{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                   p {c |}{col 38}{res}{space 2} 2.507305{col 50}{space 2} .0784717{col 78}{space 4} 2.358125{col 91}{space 3} 2.665921
{txt}                                 1/p {c |}{col 38}{res}{space 2} .3988347{col 50}{space 2} .0124824{col 78}{space 4} .3751048{col 91}{space 3} .4240657
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-830.8551{col 39}-538.3953{col 50}    28{col 58} 1132.791{col 69} 1265.985
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelC3
{txt}
{com}. 
. 
. *** COMPUTE Figure C2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [CM1−CM4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5005703{col 26}{space 2} .1035945{col 37}{space 1}   -3.34{col 46}{space 3}0.001{col 54}{space 4} .3336617{col 67}{space 3}  .750972
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC3zloyalnom = r(table)
{txt}
{com}. mat list modelC3zloyalnom
{res}
{txt}modelC3zloyalnom[9,1]
               (1)
     b {res}  .50057025
{txt}    se {res}  .10359452
{txt}     z {res} -3.3437896
{txt}pvalue {res}  .00082642
{txt}    ll {res}  .33366167
{txt}    ul {res}  .75097203
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. *
. *
. 
. estimates restore modelC3
{txt}(results {stata estimates replay modelC3:modelC3} are active now)

{com}. 
. lincomest 1.soubinaryagency2onoff#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2onoff#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .8743032{col 26}{space 2} .1260453{col 37}{space 1}   -0.93{col 46}{space 3}0.351{col 54}{space 4} .6590939{col 67}{space 3} 1.159783
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC3zloyalonoff = r(table)
{txt}
{com}. mat list modelC3zloyalonoff
{res}
{txt}modelC3zloyalonoff[9,1]
               (1)
     b {res}  .87430323
{txt}    se {res}  .12604531
{txt}     z {res} -.93175561
{txt}pvalue {res}  .35146284
{txt}    ll {res}  .65909386
{txt}    ul {res}  1.1597834
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. *
. *
. 
. estimates restore modelC3
{txt}(results {stata estimates replay modelC3:modelC3} are active now)

{com}. 
. lincomest 1.soubinaryagency2offon#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2offon#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .8053562{col 26}{space 2} .1736081{col 37}{space 1}   -1.00{col 46}{space 3}0.315{col 54}{space 4} .5278338{col 67}{space 3} 1.228793
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC3zloyaloffon = r(table)
{txt}
{com}. mat list modelC3zloyaloffon
{res}
{txt}modelC3zloyaloffon[9,1]
               (1)
     b {res}   .8053562
{txt}    se {res}  .17360807
{txt}     z {res} -1.0041927
{txt}pvalue {res}  .31528576
{txt}    ll {res}   .5278338
{txt}    ul {res}  1.2287932
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. 
. **** COMPUTE Figure C3: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [CM1−CM4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. ** Generate 'manual' interaction variables ** 
. 
. generate loyalppdiffnom = soubinaryagency2nom*zloyalmedian
{txt}
{com}. *
. generate loyalppdiffonoff = soubinaryagency2onoff*zloyalmedian
{txt}
{com}. *
. generate loyalppdiffoffon = soubinaryagency2offon*zloyalmedian
{txt}
{com}. 
. 
. 
. 
. ** Re-Estimate Model C3  with 'manual' interaction variable **
. 
. streg   zloyalmedian soubinaryagency2nom soubinaryagency2onoff soubinaryagency2offon loyalppdiffnom  loyalppdiffonoff loyalppdiffoffon  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:-621.99201}  
Iteration 2:{space 3}log pseudolikelihood = {res:-539.50361}  
Iteration 3:{space 3}log pseudolikelihood = {res:-538.39648}  
Iteration 4:{space 3}log pseudolikelihood = {res:-538.39525}  
Iteration 5:{space 3}log pseudolikelihood = {res:-538.39525}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}26{txt})    =  {res}   1320.43
{txt}Log pseudolikelihood =   {res}-538.39525             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} 1.527107{col 40}{space 2} .2159679{col 51}{space 1}    2.99{col 60}{space 3}0.003{col 68}{space 4} 1.157417{col 81}{space 3} 2.014878
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.071418{col 40}{space 2} .1560699{col 51}{space 1}    0.47{col 60}{space 3}0.636{col 68}{space 4} .8053173{col 81}{space 3} 1.425445
{txt}{space 5}soubinaryagency2onoff {c |}{col 28}{res}{space 2} 1.211724{col 40}{space 2} .1456863{col 51}{space 1}    1.60{col 60}{space 3}0.110{col 68}{space 4}  .957334{col 81}{space 3} 1.533714
{txt}{space 5}soubinaryagency2offon {c |}{col 28}{res}{space 2} .9693603{col 40}{space 2} .2034046{col 51}{space 1}   -0.15{col 60}{space 3}0.882{col 68}{space 4} .6425017{col 81}{space 3} 1.462501
{txt}{space 12}loyalppdiffnom {c |}{col 28}{res}{space 2} .6023931{col 40}{space 2} .0913096{col 51}{space 1}   -3.34{col 60}{space 3}0.001{col 68}{space 4} .4475654{col 81}{space 3} .8107808
{txt}{space 10}loyalppdiffonoff {c |}{col 28}{res}{space 2} .9062994{col 40}{space 2} .0956976{col 51}{space 1}   -0.93{col 60}{space 3}0.351{col 68}{space 4} .7368719{col 81}{space 3} 1.114683
{txt}{space 10}loyalppdiffoffon {c |}{col 28}{res}{space 2} .8533811{col 40}{space 2} .1347378{col 51}{space 1}   -1.00{col 60}{space 3}0.315{col 68}{space 4} .6262523{col 81}{space 3} 1.162885
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} .9964087{col 40}{space 2} .0705066{col 51}{space 1}   -0.05{col 60}{space 3}0.959{col 68}{space 4}  .867373{col 81}{space 3} 1.144641
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} 1.006489{col 40}{space 2} .0589626{col 51}{space 1}    0.11{col 60}{space 3}0.912{col 68}{space 4} .8973123{col 81}{space 3} 1.128949
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5992792{col 40}{space 2} .0479145{col 51}{space 1}   -6.40{col 60}{space 3}0.000{col 68}{space 4}  .512357{col 81}{space 3} .7009479
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2}  1.46751{col 40}{space 2} .1409356{col 51}{space 1}    3.99{col 60}{space 3}0.000{col 68}{space 4} 1.215722{col 81}{space 3} 1.771447
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} 1.374242{col 40}{space 2} .1448258{col 51}{space 1}    3.02{col 60}{space 3}0.003{col 68}{space 4} 1.117786{col 81}{space 3} 1.689538
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.078083{col 40}{space 2} .1679761{col 51}{space 1}    0.48{col 60}{space 3}0.629{col 68}{space 4} .7943767{col 81}{space 3} 1.463113
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} .8012574{col 40}{space 2} .0738488{col 51}{space 1}   -2.40{col 60}{space 3}0.016{col 68}{space 4} .6688366{col 81}{space 3} .9598957
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} .0004655{col 40}{space 2} .0012037{col 51}{space 1}   -2.97{col 60}{space 3}0.003{col 68}{space 4} 2.93e-06{col 81}{space 3} .0739714
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 1.925425{col 40}{space 2} .4086193{col 51}{space 1}    3.09{col 60}{space 3}0.002{col 68}{space 4} 1.270229{col 81}{space 3} 2.918576
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .1986038{col 40}{space 2} .0357571{col 51}{space 1}   -8.98{col 60}{space 3}0.000{col 68}{space 4} .1395517{col 81}{space 3} .2826442
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9951923{col 40}{space 2} .0034867{col 51}{space 1}   -1.38{col 60}{space 3}0.169{col 68}{space 4} .9883819{col 81}{space 3}  1.00205
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} .9393889{col 40}{space 2} .0431119{col 51}{space 1}   -1.36{col 60}{space 3}0.173{col 68}{space 4}   .85858{col 81}{space 3} 1.027804
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 1.852487{col 40}{space 2} .3726307{col 51}{space 1}    3.06{col 60}{space 3}0.002{col 68}{space 4}  1.24892{col 81}{space 3}  2.74774
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 5.852259{col 40}{space 2} .7982627{col 51}{space 1}   12.95{col 60}{space 3}0.000{col 68}{space 4} 4.479376{col 81}{space 3} 7.645916
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 4.898927{col 40}{space 2} 1.495417{col 51}{space 1}    5.21{col 60}{space 3}0.000{col 68}{space 4} 2.693198{col 81}{space 3} 8.911147
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.350267{col 40}{space 2} .1797599{col 51}{space 1}    2.26{col 60}{space 3}0.024{col 68}{space 4} 1.040159{col 81}{space 3} 1.752828
{txt}{space 24}6  {c |}{col 28}{res}{space 2}  2.78022{col 40}{space 2} .3435877{col 51}{space 1}    8.27{col 60}{space 3}0.000{col 68}{space 4} 2.182153{col 81}{space 3} 3.542201
{txt}{space 24}7  {c |}{col 28}{res}{space 2}  5.92597{col 40}{space 2} 1.409356{col 51}{space 1}    7.48{col 60}{space 3}0.000{col 68}{space 4} 3.718097{col 81}{space 3} 9.444918
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 8.902593{col 40}{space 2} 2.590367{col 51}{space 1}    7.51{col 60}{space 3}0.000{col 68}{space 4} 5.033202{col 81}{space 3} 15.74667
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 3.13e-06{col 40}{space 2} 6.04e-06{col 51}{space 1}   -6.57{col 60}{space 3}0.000{col 68}{space 4} 7.15e-08{col 81}{space 3} .0001373
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} .9192083{col 40}{space 2} .0312972{col 51}{space 1}   29.37{col 60}{space 3}0.000{col 68}{space 4} .8578669{col 81}{space 3} .9805497
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 2.507305{col 40}{space 2} .0784717{col 68}{space 4} 2.358125{col 81}{space 3} 2.665921
{txt}                       1/p {c |}{col 28}{res}{space 2} .3988347{col 40}{space 2} .0124824{col 68}{space 4} .3751048{col 81}{space 3} .4240657
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimate store modelC3a
{txt}
{com}. 
. 
. 
. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. 
. *Interquartile range
. 
. margins, predict(median time) at(loyalppdiffnom=(-0.3960373 0.9692858))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~m}{space 4}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~m}{space 4}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     9.74{col 38}{space 2}   0.0018
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 285.6718{col 26}{space 2} 91.51514{col 37}{space 5} 106.3054{col 51}{space 3} 465.0382
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. matrix modelC3azloyalnom = r(table)
{txt}
{com}. mat list modelC3azloyalnom
{res}
{txt}modelC3azloyalnom[9,1]
            r2vs1.
              _at
     b {res} 285.67179
{txt}    se {res} 91.515141
{txt}     z {res} 3.1215795
{txt}pvalue {res} .00179884
{txt}    ll {res} 106.30541
{txt}    ul {res} 465.03817
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. *
. *
. margins, predict(median time) at(loyalppdiffonoff=(-0.3960373 0.9692858))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~f}{space 4}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~f}{space 4}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     0.84{col 38}{space 2}   0.3596
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 53.46566{col 26}{space 2} 58.36411{col 37}{space 5}-60.92589{col 51}{space 3} 167.8572
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. matrix modelC3azloyalonoff = r(table)
{txt}
{com}. mat list modelC3azloyalonoff
{res}
{txt}modelC3azloyalonoff[9,1]
             r2vs1.
               _at
     b {res}  53.465663
{txt}    se {res}  58.364111
{txt}     z {res}  .91607089
{txt}pvalue {res}  .35962971
{txt}    ll {res} -60.925892
{txt}    ul {res}  167.85722
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. *
. *
. margins, predict(median time) at(loyalppdiffoffon=(-0.3960373 0.9692858))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~n}{space 4}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~n}{space 4}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     0.96{col 38}{space 2}   0.3274
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2}  86.7933{col 26}{space 2} 88.62291{col 37}{space 5} -86.9044{col 51}{space 3}  260.491
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. matrix modelC3azloyaloffon = r(table)
{txt}
{com}. mat list modelC3azloyaloffon
{res}
{txt}modelC3azloyaloffon[9,1]
             r2vs1.
               _at
     b {res}  86.793302
{txt}    se {res}  88.622906
{txt}     z {res}  .97935518
{txt}pvalue {res}  .32740452
{txt}    ll {res} -86.904402
{txt}    ul {res}  260.49101
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. 
. 
. *Interdecile range
. 
. estimates restore modelC3a
{txt}(results {stata estimates replay modelC3a:modelC3a} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiffnom=(-0.6451644 1.711348))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~m}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~m}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 854.6398{col 26}{space 2} 36.19887{col 37}{space 1}   23.61{col 46}{space 3}0.000{col 54}{space 4} 783.6913{col 67}{space 3} 925.5882
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1376.147{col 26}{space 2} 144.7636{col 37}{space 1}    9.51{col 46}{space 3}0.000{col 54}{space 4} 1092.416{col 67}{space 3} 1659.879
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiffnom=(-0.6451644 1.711348))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~m}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~m}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     8.70{col 38}{space 2}   0.0032
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 521.5073{col 26}{space 2} 176.8256{col 37}{space 5} 174.9356{col 51}{space 3} 868.0791
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelC3bzloyalnom = r(table)
{txt}
{com}. mat list modelC3bzloyalnom
{res}
{txt}modelC3bzloyalnom[9,1]
            r2vs1.
              _at
     b {res} 521.50732
{txt}    se {res} 176.82558
{txt}     z {res} 2.9492753
{txt}pvalue {res}  .0031852
{txt}    ll {res} 174.93555
{txt}    ul {res} 868.07909
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. *
. *
. 
. margins, predict(median time) at(loyalppdiffonoff=(-0.6451644 1.711348))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~f}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~f}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 962.0207{col 26}{space 2} 33.22838{col 37}{space 1}   28.95{col 46}{space 3}0.000{col 54}{space 4} 896.8942{col 67}{space 3} 1027.147
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  1055.22{col 26}{space 2} 74.68483{col 37}{space 1}   14.13{col 46}{space 3}0.000{col 54}{space 4} 908.8403{col 67}{space 3} 1201.599
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiffonoff=(-0.6451644 1.711348))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~f}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~f}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     0.82{col 38}{space 2}   0.3644
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 93.19921{col 26}{space 2} 102.7615{col 37}{space 5}-108.2096{col 51}{space 3} 294.6081
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelC3bzloyalonoff = r(table)
{txt}
{com}. mat list modelC3bzloyalonoff
{res}
{txt}modelC3bzloyalonoff[9,1]
             r2vs1.
               _at
     b {res}  93.199207
{txt}    se {res}   102.7615
{txt}     z {res}   .9069467
{txt}pvalue {res}  .36443499
{txt}    ll {res} -108.20964
{txt}    ul {res}  294.60805
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. *
. *
. 
. margins, predict(median time) at(loyalppdiffoffon=(-0.6451644 1.711348))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~n}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~n}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 947.4803{col 26}{space 2}  38.7251{col 37}{space 1}   24.47{col 46}{space 3}0.000{col 54}{space 4} 871.5805{col 67}{space 3}  1023.38
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  1099.73{col 26}{space 2} 123.0631{col 37}{space 1}    8.94{col 46}{space 3}0.000{col 54}{space 4} 858.5306{col 67}{space 3} 1340.929
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiffoffon=(-0.6451644 1.711348))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~n}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~n}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     0.93{col 38}{space 2}   0.3353
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 152.2495{col 26}{space 2} 158.0173{col 37}{space 5}-157.4587{col 51}{space 3} 461.9578
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelC3bzloyaloffon = r(table)
{txt}
{com}. mat list modelC3bzloyaloffon
{res}
{txt}modelC3bzloyaloffon[9,1]
             r2vs1.
               _at
     b {res}  152.24954
{txt}    se {res}  158.01733
{txt}     z {res}  .96349902
{txt}pvalue {res}  .33529716
{txt}    ll {res} -157.45873
{txt}    ul {res}  461.95782
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. **** MODEL C4: WEIBULL MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom   c.zloyalmedian##i.soubinaryagency2onoff  c.zloyalmedian##i.soubinaryagency2offon   zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr  i.sbagency reagan bush41 clinton bush43, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: zloyalmedian omitted because of collinearity
note: zloyalmedian omitted because of collinearity
note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:-604.68539}  
Iteration 2:{space 3}log pseudolikelihood = {res:-498.26361}  
Iteration 3:{space 3}log pseudolikelihood = {res:-496.94454}  
Iteration 4:{space 3}log pseudolikelihood = {res:-496.94155}  
Iteration 5:{space 3}log pseudolikelihood = {res:-496.94155}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
{col 49}{help j_robustsingular##|_new:Wald chi2(26)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-496.94155             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 102:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                                  _t{col 38}{c |} Haz. Ratio{col 50}   Std. Err.{col 62}      z{col 70}   P>|z|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}zloyalmedian {c |}{col 38}{res}{space 2} 1.392237{col 50}{space 2} .2278874{col 61}{space 1}    2.02{col 70}{space 3}0.043{col 78}{space 4} 1.010147{col 91}{space 3} 1.918852
{txt}{space 15}1.soubinaryagency2nom {c |}{col 38}{res}{space 2} 1.047373{col 50}{space 2} .2402527{col 61}{space 1}    0.20{col 70}{space 3}0.840{col 78}{space 4}   .66811{col 91}{space 3} 1.641932
{txt}{space 36} {c |}
{space 2}soubinaryagency2nom#c.zloyalmedian {c |}
{space 34}1  {c |}{col 38}{res}{space 2}  .636256{col 50}{space 2} .1191458{col 61}{space 1}   -2.41{col 70}{space 3}0.016{col 78}{space 4} .4407936{col 91}{space 3} .9183929
{txt}{space 36} {c |}
{space 24}zloyalmedian {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 13}1.soubinaryagency2onoff {c |}{col 38}{res}{space 2} 1.121597{col 50}{space 2} .1713457{col 61}{space 1}    0.75{col 70}{space 3}0.453{col 78}{space 4}  .831379{col 91}{space 3} 1.513123
{txt}{space 36} {c |}
soubinaryagency2onoff#c.zloyalmedian {c |}
{space 34}1  {c |}{col 38}{res}{space 2} .8612187{col 50}{space 2} .1272686{col 61}{space 1}   -1.01{col 70}{space 3}0.312{col 78}{space 4} .6446518{col 91}{space 3}  1.15054
{txt}{space 36} {c |}
{space 24}zloyalmedian {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 13}1.soubinaryagency2offon {c |}{col 38}{res}{space 2} .8913277{col 50}{space 2} .2252523{col 61}{space 1}   -0.46{col 70}{space 3}0.649{col 78}{space 4} .5431572{col 91}{space 3}  1.46268
{txt}{space 36} {c |}
soubinaryagency2offon#c.zloyalmedian {c |}
{space 34}1  {c |}{col 38}{res}{space 2} .9858189{col 50}{space 2} .1781196{col 61}{space 1}   -0.08{col 70}{space 3}0.937{col 78}{space 4} .6918317{col 91}{space 3} 1.404733
{txt}{space 36} {c |}
{space 23}zpecompmedian {c |}{col 38}{res}{space 2} 1.044505{col 50}{space 2} .0809547{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .8973008{col 91}{space 3} 1.215859
{txt}{space 23}zmecompmedian {c |}{col 38}{res}{space 2} .9822845{col 50}{space 2} .0663984{col 61}{space 1}   -0.26{col 70}{space 3}0.791{col 78}{space 4} .8603984{col 91}{space 3} 1.121437
{txt}{space 27}toplevel2 {c |}{col 38}{res}{space 2} .5377659{col 50}{space 2} .0556301{col 61}{space 1}   -6.00{col 70}{space 3}0.000{col 78}{space 4} .4390756{col 91}{space 3} .6586386
{txt}{space 16}presagencyideolalign {c |}{col 38}{res}{space 2} .7306045{col 50}{space 2} .1828141{col 61}{space 1}   -1.25{col 70}{space 3}0.210{col 78}{space 4} .4473959{col 91}{space 3} 1.193088
{txt}{space 14}presagencyideolopposed {c |}{col 38}{res}{space 2} .6962598{col 50}{space 2}  .178807{col 61}{space 1}   -1.41{col 70}{space 3}0.159{col 78}{space 4} .4208949{col 91}{space 3} 1.151779
{txt}{space 21}subagencydesign {c |}{col 38}{res}{space 2} 1.595164{col 50}{space 2} .3355596{col 61}{space 1}    2.22{col 70}{space 3}0.026{col 78}{space 4} 1.056199{col 91}{space 3} 2.409154
{txt}{space 14}standaloneagencydesign {c |}{col 38}{res}{space 2} 1.755764{col 50}{space 2} .4897302{col 61}{space 1}    2.02{col 70}{space 3}0.044{col 78}{space 4} 1.016349{col 91}{space 3} 3.033118
{txt}{space 10}okstartsenpolarizationmean {c |}{col 38}{res}{space 2} 4.77e-11{col 50}{space 2} 4.85e-10{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} 1.03e-19{col 91}{space 3}    .0221
{txt}{space 13}okstartfilipresdistance {c |}{col 38}{res}{space 2} 948.6663{col 50}{space 2} 2084.671{col 61}{space 1}    3.12{col 70}{space 3}0.002{col 78}{space 4} 12.78258{col 91}{space 3} 70405.78
{txt}{space 25}okcrossover {c |}{col 38}{res}{space 2} .1736654{col 50}{space 2} .0373255{col 61}{space 1}   -8.15{col 70}{space 3}0.000{col 78}{space 4} .1139637{col 91}{space 3}  .264643
{txt}{space 22}okstartpresapp {c |}{col 38}{res}{space 2} .9900845{col 50}{space 2} .0044931{col 61}{space 1}   -2.20{col 70}{space 3}0.028{col 78}{space 4} .9813173{col 91}{space 3} .9989301
{txt}{space 17}okstartunemployment {c |}{col 38}{res}{space 2} 1.136843{col 50}{space 2}  .096974{col 61}{space 1}    1.50{col 70}{space 3}0.133{col 78}{space 4} .9618163{col 91}{space 3} 1.343721
{txt}{space 36} {c |}
{space 25}okstartadyr {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.645241{col 50}{space 2} .3698717{col 61}{space 1}    2.21{col 70}{space 3}0.027{col 78}{space 4} 1.058933{col 91}{space 3} 2.556176
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 4.387259{col 50}{space 2} .9198416{col 61}{space 1}    7.05{col 70}{space 3}0.000{col 78}{space 4} 2.908899{col 91}{space 3} 6.616951
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 3.965649{col 50}{space 2} 1.235294{col 61}{space 1}    4.42{col 70}{space 3}0.000{col 78}{space 4} 2.153605{col 91}{space 3} 7.302345
{txt}{space 34}5  {c |}{col 38}{res}{space 2} 1.548603{col 50}{space 2} .3881749{col 61}{space 1}    1.74{col 70}{space 3}0.081{col 78}{space 4} .9474944{col 91}{space 3} 2.531065
{txt}{space 34}6  {c |}{col 38}{res}{space 2} 3.500579{col 50}{space 2} .8890604{col 61}{space 1}    4.93{col 70}{space 3}0.000{col 78}{space 4} 2.127922{col 91}{space 3} 5.758694
{txt}{space 34}7  {c |}{col 38}{res}{space 2} 6.396918{col 50}{space 2} 1.904906{col 61}{space 1}    6.23{col 70}{space 3}0.000{col 78}{space 4} 3.568582{col 91}{space 3}  11.4669
{txt}{space 34}8  {c |}{col 38}{res}{space 2} 10.19087{col 50}{space 2} 3.827624{col 61}{space 1}    6.18{col 70}{space 3}0.000{col 78}{space 4} 4.880962{col 91}{space 3} 21.27732
{txt}{space 36} {c |}
{space 28}sbagency {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 2.892469{col 50}{space 2}  .801117{col 61}{space 1}    3.83{col 70}{space 3}0.000{col 78}{space 4} 1.680793{col 91}{space 3} 4.977635
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.885482{col 50}{space 2} .4982347{col 61}{space 1}    2.40{col 70}{space 3}0.016{col 78}{space 4} 1.123296{col 91}{space 3} 3.164832
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.337829{col 50}{space 2} .3337726{col 61}{space 1}    1.17{col 70}{space 3}0.243{col 78}{space 4} .8204196{col 91}{space 3} 2.181551
{txt}{space 34}5  {c |}{col 38}{res}{space 2} 1.044919{col 50}{space 2} .2800139{col 61}{space 1}    0.16{col 70}{space 3}0.870{col 78}{space 4} .6179884{col 91}{space 3} 1.766789
{txt}{space 34}6  {c |}{col 38}{res}{space 2}  2.72138{col 50}{space 2}  .710254{col 61}{space 1}    3.84{col 70}{space 3}0.000{col 78}{space 4} 1.631675{col 91}{space 3} 4.538837
{txt}{space 34}7  {c |}{col 38}{res}{space 2} 1.855858{col 50}{space 2}  .564342{col 61}{space 1}    2.03{col 70}{space 3}0.042{col 78}{space 4} 1.022599{col 91}{space 3} 3.368092
{txt}{space 34}8  {c |}{col 38}{res}{space 2} 2.401702{col 50}{space 2} .6335844{col 61}{space 1}    3.32{col 70}{space 3}0.001{col 78}{space 4} 1.432078{col 91}{space 3} 4.027834
{txt}{space 34}9  {c |}{col 38}{res}{space 2} 2.269411{col 50}{space 2} .5932062{col 61}{space 1}    3.14{col 70}{space 3}0.002{col 78}{space 4} 1.359614{col 91}{space 3} 3.788005
{txt}{space 33}11  {c |}{col 38}{res}{space 2} 3.841208{col 50}{space 2} 1.208632{col 61}{space 1}    4.28{col 70}{space 3}0.000{col 78}{space 4} 2.073186{col 91}{space 3}  7.11701
{txt}{space 33}12  {c |}{col 38}{res}{space 2} 1.665106{col 50}{space 2} .3084369{col 61}{space 1}    2.75{col 70}{space 3}0.006{col 78}{space 4} 1.158161{col 91}{space 3} 2.393948
{txt}{space 33}13  {c |}{col 38}{res}{space 2} 1.516094{col 50}{space 2} .3639813{col 61}{space 1}    1.73{col 70}{space 3}0.083{col 78}{space 4}  .947046{col 91}{space 3} 2.427063
{txt}{space 33}14  {c |}{col 38}{res}{space 2} 2.472841{col 50}{space 2} .6944199{col 61}{space 1}    3.22{col 70}{space 3}0.001{col 78}{space 4} 1.426143{col 91}{space 3}  4.28775
{txt}{space 33}15  {c |}{col 38}{res}{space 2} 1.676558{col 50}{space 2} .4479327{col 61}{space 1}    1.93{col 70}{space 3}0.053{col 78}{space 4} .9931151{col 91}{space 3} 2.830333
{txt}{space 33}16  {c |}{col 38}{res}{space 2}  .880794{col 50}{space 2} .1540305{col 61}{space 1}   -0.73{col 70}{space 3}0.468{col 78}{space 4} .6251998{col 91}{space 3}  1.24088
{txt}{space 33}17  {c |}{col 38}{res}{space 2} 1.654225{col 50}{space 2} .1398663{col 61}{space 1}    5.95{col 70}{space 3}0.000{col 78}{space 4} 1.401602{col 91}{space 3} 1.952381
{txt}{space 33}18  {c |}{col 38}{res}{space 2} 2.015493{col 50}{space 2} .5869484{col 61}{space 1}    2.41{col 70}{space 3}0.016{col 78}{space 4} 1.138927{col 91}{space 3} 3.566701
{txt}{space 33}19  {c |}{col 38}{res}{space 2} .7865134{col 50}{space 2} .1134181{col 61}{space 1}   -1.67{col 70}{space 3}0.096{col 78}{space 4} .5928704{col 91}{space 3} 1.043404
{txt}{space 33}20  {c |}{col 38}{res}{space 2} .3109688{col 50}{space 2} .0956504{col 61}{space 1}   -3.80{col 70}{space 3}0.000{col 78}{space 4} .1701755{col 91}{space 3} .5682463
{txt}{space 33}21  {c |}{col 38}{res}{space 2} .8395588{col 50}{space 2} .0964631{col 61}{space 1}   -1.52{col 70}{space 3}0.128{col 78}{space 4} .6702708{col 91}{space 3} 1.051603
{txt}{space 33}22  {c |}{col 38}{res}{space 2} .4844347{col 50}{space 2} .1673371{col 61}{space 1}   -2.10{col 70}{space 3}0.036{col 78}{space 4} .2461539{col 91}{space 3} .9533751
{txt}{space 33}23  {c |}{col 38}{res}{space 2} 1.101179{col 50}{space 2} .2872981{col 61}{space 1}    0.37{col 70}{space 3}0.712{col 78}{space 4} .6603573{col 91}{space 3} 1.836271
{txt}{space 33}24  {c |}{col 38}{res}{space 2} .3223595{col 50}{space 2} .1368343{col 61}{space 1}   -2.67{col 70}{space 3}0.008{col 78}{space 4} .1402893{col 91}{space 3} .7407237
{txt}{space 33}25  {c |}{col 38}{res}{space 2}  1.45171{col 50}{space 2} .2402648{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.049548{col 91}{space 3} 2.007973
{txt}{space 33}26  {c |}{col 38}{res}{space 2} .8100944{col 50}{space 2} .1410504{col 61}{space 1}   -1.21{col 70}{space 3}0.226{col 78}{space 4} .5758745{col 91}{space 3} 1.139576
{txt}{space 33}27  {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 33}28  {c |}{col 38}{res}{space 2} 1.296314{col 50}{space 2} .2160065{col 61}{space 1}    1.56{col 70}{space 3}0.119{col 78}{space 4} .9351335{col 91}{space 3} 1.796996
{txt}{space 33}29  {c |}{col 38}{res}{space 2} 3.455913{col 50}{space 2} 1.149684{col 61}{space 1}    3.73{col 70}{space 3}0.000{col 78}{space 4} 1.800496{col 91}{space 3} 6.633357
{txt}{space 33}30  {c |}{col 38}{res}{space 2} 1.490025{col 50}{space 2} .4471542{col 61}{space 1}    1.33{col 70}{space 3}0.184{col 78}{space 4} .8274642{col 91}{space 3} 2.683106
{txt}{space 33}50  {c |}{col 38}{res}{space 2} 1.963156{col 50}{space 2} .3900306{col 61}{space 1}    3.40{col 70}{space 3}0.001{col 78}{space 4}  1.32997{col 91}{space 3} 2.897794
{txt}{space 33}51  {c |}{col 38}{res}{space 2} 3.170232{col 50}{space 2} .8547286{col 61}{space 1}    4.28{col 70}{space 3}0.000{col 78}{space 4} 1.868952{col 91}{space 3} 5.377546
{txt}{space 33}52  {c |}{col 38}{res}{space 2} 1.525858{col 50}{space 2} .5291799{col 61}{space 1}    1.22{col 70}{space 3}0.223{col 78}{space 4} .7732336{col 91}{space 3} 3.011048
{txt}{space 33}53  {c |}{col 38}{res}{space 2} 1.505091{col 50}{space 2} .1632623{col 61}{space 1}    3.77{col 70}{space 3}0.000{col 78}{space 4}  1.21683{col 91}{space 3} 1.861639
{txt}{space 33}54  {c |}{col 38}{res}{space 2} 1.660778{col 50}{space 2} .3217494{col 61}{space 1}    2.62{col 70}{space 3}0.009{col 78}{space 4}  1.13607{col 91}{space 3} 2.427831
{txt}{space 33}55  {c |}{col 38}{res}{space 2}  1.17161{col 50}{space 2} .4401006{col 61}{space 1}    0.42{col 70}{space 3}0.673{col 78}{space 4} .5610992{col 91}{space 3} 2.446393
{txt}{space 33}56  {c |}{col 38}{res}{space 2} 1.100927{col 50}{space 2} .4399089{col 61}{space 1}    0.24{col 70}{space 3}0.810{col 78}{space 4} .5030777{col 91}{space 3} 2.409251
{txt}{space 33}57  {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 33}58  {c |}{col 38}{res}{space 2}  1.25028{col 50}{space 2} .4749557{col 61}{space 1}    0.59{col 70}{space 3}0.557{col 78}{space 4}  .593818{col 91}{space 3} 2.632457
{txt}{space 33}59  {c |}{col 38}{res}{space 2} .3820146{col 50}{space 2} .0958744{col 61}{space 1}   -3.83{col 70}{space 3}0.000{col 78}{space 4} .2335896{col 91}{space 3} .6247501
{txt}{space 33}60  {c |}{col 38}{res}{space 2} .9488226{col 50}{space 2} .1392513{col 61}{space 1}   -0.36{col 70}{space 3}0.720{col 78}{space 4} .7116407{col 91}{space 3} 1.265055
{txt}{space 33}61  {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 36} {c |}
{space 30}reagan {c |}{col 38}{res}{space 2} .0617049{col 50}{space 2} .0572943{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .0099991{col 91}{space 3} .3807841
{txt}{space 30}bush41 {c |}{col 38}{res}{space 2} .1551096{col 50}{space 2} .0936646{col 61}{space 1}   -3.09{col 70}{space 3}0.002{col 78}{space 4} .0474932{col 91}{space 3} .5065774
{txt}{space 29}clinton {c |}{col 38}{res}{space 2} .6340865{col 50}{space 2} .3300955{col 61}{space 1}   -0.88{col 70}{space 3}0.382{col 78}{space 4} .2285737{col 91}{space 3}  1.75902
{txt}{space 30}bush43 {c |}{col 38}{res}{space 2} .2181921{col 50}{space 2} .1564885{col 61}{space 1}   -2.12{col 70}{space 3}0.034{col 78}{space 4} .0534999{col 91}{space 3} .8898671
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .0004364{col 50}{space 2} .0023087{col 61}{space 1}   -1.46{col 70}{space 3}0.144{col 78}{space 4} 1.37e-08{col 91}{space 3} 13.89843
{txt}{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 31}/ln_p {c |}{col 38}{res}{space 2} .9886731{col 50}{space 2} .0303433{col 61}{space 1}   32.58{col 70}{space 3}0.000{col 78}{space 4} .9292014{col 91}{space 3} 1.048145
{txt}{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                   p {c |}{col 38}{res}{space 2} 2.687666{col 50}{space 2} .0815526{col 78}{space 4} 2.532486{col 91}{space 3} 2.852355
{txt}                                 1/p {c |}{col 38}{res}{space 2}   .37207{col 50}{space 2} .0112898{col 78}{space 4} .3505875{col 91}{space 3} .3948689
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-830.8551{col 39}-496.9415{col 50}    28{col 58} 1049.883{col 69} 1183.077
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelC4
{txt}
{com}. 
. 
. 
. *** COMPUTE Figure C2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [CM1−CM4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .539379{col 26}{space 2} .1379038{col 37}{space 1}   -2.41{col 46}{space 3}0.016{col 54}{space 4} .3267881{col 67}{space 3} .8902704
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC4zloyalnom = r(table)
{txt}
{com}. mat list modelC4zloyalnom
{res}
{txt}modelC4zloyalnom[9,1]
              (1)
     b {res} .53937905
{txt}    se {res} .13790379
{txt}     z {res} -2.414571
{txt}pvalue {res} .01575375
{txt}    ll {res} .32678807
{txt}    ul {res} .89027044
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         1
{reset}
{com}. *
. *
. 
. estimates restore modelC4
{txt}(results {stata estimates replay modelC4:modelC4} are active now)

{com}. 
. lincomest 1.soubinaryagency2onoff#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2onoff#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .8154717{col 26}{space 2} .1645326{col 37}{space 1}   -1.01{col 46}{space 3}0.312{col 54}{space 4}   .54912{col 67}{space 3} 1.211018
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC4zloyalonoff = r(table)
{txt}
{com}. mat list modelC4zloyalonoff
{res}
{txt}modelC4zloyalonoff[9,1]
               (1)
     b {res}  .81547174
{txt}    se {res}  .16453264
{txt}     z {res} -1.0110266
{txt}pvalue {res}  .31200371
{txt}    ll {res}  .54912005
{txt}    ul {res}  1.2110178
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. *
. *
. 
. estimates restore modelC4
{txt}(results {stata estimates replay modelC4:modelC4} are active now)

{com}. 
. lincomest 1.soubinaryagency2offon#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2offon#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .9806885{col 26}{space 2} .2419252{col 37}{space 1}   -0.08{col 46}{space 3}0.937{col 54}{space 4} .6047125{col 67}{space 3} 1.590425
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC4zloyaloffon = r(table)
{txt}
{com}. mat list modelC4zloyaloffon
{res}
{txt}modelC4zloyaloffon[9,1]
               (1)
     b {res}  .98068846
{txt}    se {res}  .24192516
{txt}     z {res} -.07904867
{txt}pvalue {res}  .93699391
{txt}    ll {res}  .60471246
{txt}    ul {res}  1.5904251
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. **** COMPUTE Figure C3: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [CM1−CM4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. ** Re-Estimate Model C4  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom soubinaryagency2onoff soubinaryagency2offon loyalppdiffnom  loyalppdiffonoff loyalppdiffoffon   zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.sbagency reagan bush41 clinton bush43, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:-604.68539}  
Iteration 2:{space 3}log pseudolikelihood = {res:-498.26361}  
Iteration 3:{space 3}log pseudolikelihood = {res:-496.94454}  
Iteration 4:{space 3}log pseudolikelihood = {res:-496.94155}  
Iteration 5:{space 3}log pseudolikelihood = {res:-496.94155}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
{col 49}{help j_robustsingular##|_new:Wald chi2(26)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-496.94155             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} 1.392237{col 40}{space 2} .2278874{col 51}{space 1}    2.02{col 60}{space 3}0.043{col 68}{space 4} 1.010147{col 81}{space 3} 1.918852
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.047373{col 40}{space 2} .2402527{col 51}{space 1}    0.20{col 60}{space 3}0.840{col 68}{space 4}   .66811{col 81}{space 3} 1.641932
{txt}{space 5}soubinaryagency2onoff {c |}{col 28}{res}{space 2} 1.121597{col 40}{space 2} .1713457{col 51}{space 1}    0.75{col 60}{space 3}0.453{col 68}{space 4}  .831379{col 81}{space 3} 1.513123
{txt}{space 5}soubinaryagency2offon {c |}{col 28}{res}{space 2} .8913277{col 40}{space 2} .2252523{col 51}{space 1}   -0.46{col 60}{space 3}0.649{col 68}{space 4} .5431572{col 81}{space 3}  1.46268
{txt}{space 12}loyalppdiffnom {c |}{col 28}{res}{space 2}  .636256{col 40}{space 2} .1191458{col 51}{space 1}   -2.41{col 60}{space 3}0.016{col 68}{space 4} .4407936{col 81}{space 3} .9183929
{txt}{space 10}loyalppdiffonoff {c |}{col 28}{res}{space 2} .8612187{col 40}{space 2} .1272686{col 51}{space 1}   -1.01{col 60}{space 3}0.312{col 68}{space 4} .6446518{col 81}{space 3}  1.15054
{txt}{space 10}loyalppdiffoffon {c |}{col 28}{res}{space 2} .9858189{col 40}{space 2} .1781196{col 51}{space 1}   -0.08{col 60}{space 3}0.937{col 68}{space 4} .6918317{col 81}{space 3} 1.404733
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} 1.044505{col 40}{space 2} .0809547{col 51}{space 1}    0.56{col 60}{space 3}0.574{col 68}{space 4} .8973008{col 81}{space 3} 1.215859
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} .9822845{col 40}{space 2} .0663984{col 51}{space 1}   -0.26{col 60}{space 3}0.791{col 68}{space 4} .8603984{col 81}{space 3} 1.121437
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5377659{col 40}{space 2} .0556301{col 51}{space 1}   -6.00{col 60}{space 3}0.000{col 68}{space 4} .4390756{col 81}{space 3} .6586386
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} .7306045{col 40}{space 2} .1828141{col 51}{space 1}   -1.25{col 60}{space 3}0.210{col 68}{space 4} .4473959{col 81}{space 3} 1.193088
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} .6962598{col 40}{space 2}  .178807{col 51}{space 1}   -1.41{col 60}{space 3}0.159{col 68}{space 4} .4208949{col 81}{space 3} 1.151779
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.595164{col 40}{space 2} .3355596{col 51}{space 1}    2.22{col 60}{space 3}0.026{col 68}{space 4} 1.056199{col 81}{space 3} 2.409154
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} 1.755764{col 40}{space 2} .4897302{col 51}{space 1}    2.02{col 60}{space 3}0.044{col 68}{space 4} 1.016349{col 81}{space 3} 3.033118
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} 4.77e-11{col 40}{space 2} 4.85e-10{col 51}{space 1}   -2.33{col 60}{space 3}0.020{col 68}{space 4} 1.03e-19{col 81}{space 3}    .0221
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 948.6663{col 40}{space 2} 2084.671{col 51}{space 1}    3.12{col 60}{space 3}0.002{col 68}{space 4} 12.78258{col 81}{space 3} 70405.78
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .1736654{col 40}{space 2} .0373255{col 51}{space 1}   -8.15{col 60}{space 3}0.000{col 68}{space 4} .1139637{col 81}{space 3}  .264643
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9900845{col 40}{space 2} .0044931{col 51}{space 1}   -2.20{col 60}{space 3}0.028{col 68}{space 4} .9813173{col 81}{space 3} .9989301
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} 1.136843{col 40}{space 2}  .096974{col 51}{space 1}    1.50{col 60}{space 3}0.133{col 68}{space 4} .9618163{col 81}{space 3} 1.343721
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 1.645241{col 40}{space 2} .3698717{col 51}{space 1}    2.21{col 60}{space 3}0.027{col 68}{space 4} 1.058933{col 81}{space 3} 2.556176
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 4.387259{col 40}{space 2} .9198416{col 51}{space 1}    7.05{col 60}{space 3}0.000{col 68}{space 4} 2.908899{col 81}{space 3} 6.616951
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 3.965649{col 40}{space 2} 1.235294{col 51}{space 1}    4.42{col 60}{space 3}0.000{col 68}{space 4} 2.153605{col 81}{space 3} 7.302345
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.548603{col 40}{space 2} .3881749{col 51}{space 1}    1.74{col 60}{space 3}0.081{col 68}{space 4} .9474944{col 81}{space 3} 2.531065
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 3.500579{col 40}{space 2} .8890604{col 51}{space 1}    4.93{col 60}{space 3}0.000{col 68}{space 4} 2.127922{col 81}{space 3} 5.758694
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 6.396918{col 40}{space 2} 1.904906{col 51}{space 1}    6.23{col 60}{space 3}0.000{col 68}{space 4} 3.568582{col 81}{space 3}  11.4669
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 10.19087{col 40}{space 2} 3.827624{col 51}{space 1}    6.18{col 60}{space 3}0.000{col 68}{space 4} 4.880962{col 81}{space 3} 21.27732
{txt}{space 26} {c |}
{space 18}sbagency {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 2.892469{col 40}{space 2}  .801117{col 51}{space 1}    3.83{col 60}{space 3}0.000{col 68}{space 4} 1.680793{col 81}{space 3} 4.977635
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 1.885482{col 40}{space 2} .4982347{col 51}{space 1}    2.40{col 60}{space 3}0.016{col 68}{space 4} 1.123296{col 81}{space 3} 3.164832
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 1.337829{col 40}{space 2} .3337726{col 51}{space 1}    1.17{col 60}{space 3}0.243{col 68}{space 4} .8204196{col 81}{space 3} 2.181551
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.044919{col 40}{space 2} .2800139{col 51}{space 1}    0.16{col 60}{space 3}0.870{col 68}{space 4} .6179884{col 81}{space 3} 1.766789
{txt}{space 24}6  {c |}{col 28}{res}{space 2}  2.72138{col 40}{space 2}  .710254{col 51}{space 1}    3.84{col 60}{space 3}0.000{col 68}{space 4} 1.631675{col 81}{space 3} 4.538837
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 1.855858{col 40}{space 2}  .564342{col 51}{space 1}    2.03{col 60}{space 3}0.042{col 68}{space 4} 1.022599{col 81}{space 3} 3.368092
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 2.401702{col 40}{space 2} .6335844{col 51}{space 1}    3.32{col 60}{space 3}0.001{col 68}{space 4} 1.432078{col 81}{space 3} 4.027834
{txt}{space 24}9  {c |}{col 28}{res}{space 2} 2.269411{col 40}{space 2} .5932062{col 51}{space 1}    3.14{col 60}{space 3}0.002{col 68}{space 4} 1.359614{col 81}{space 3} 3.788005
{txt}{space 23}11  {c |}{col 28}{res}{space 2} 3.841208{col 40}{space 2} 1.208632{col 51}{space 1}    4.28{col 60}{space 3}0.000{col 68}{space 4} 2.073186{col 81}{space 3}  7.11701
{txt}{space 23}12  {c |}{col 28}{res}{space 2} 1.665106{col 40}{space 2} .3084369{col 51}{space 1}    2.75{col 60}{space 3}0.006{col 68}{space 4} 1.158161{col 81}{space 3} 2.393948
{txt}{space 23}13  {c |}{col 28}{res}{space 2} 1.516094{col 40}{space 2} .3639813{col 51}{space 1}    1.73{col 60}{space 3}0.083{col 68}{space 4}  .947046{col 81}{space 3} 2.427063
{txt}{space 23}14  {c |}{col 28}{res}{space 2} 2.472841{col 40}{space 2} .6944199{col 51}{space 1}    3.22{col 60}{space 3}0.001{col 68}{space 4} 1.426143{col 81}{space 3}  4.28775
{txt}{space 23}15  {c |}{col 28}{res}{space 2} 1.676558{col 40}{space 2} .4479327{col 51}{space 1}    1.93{col 60}{space 3}0.053{col 68}{space 4} .9931151{col 81}{space 3} 2.830333
{txt}{space 23}16  {c |}{col 28}{res}{space 2}  .880794{col 40}{space 2} .1540305{col 51}{space 1}   -0.73{col 60}{space 3}0.468{col 68}{space 4} .6251998{col 81}{space 3}  1.24088
{txt}{space 23}17  {c |}{col 28}{res}{space 2} 1.654225{col 40}{space 2} .1398663{col 51}{space 1}    5.95{col 60}{space 3}0.000{col 68}{space 4} 1.401602{col 81}{space 3} 1.952381
{txt}{space 23}18  {c |}{col 28}{res}{space 2} 2.015493{col 40}{space 2} .5869484{col 51}{space 1}    2.41{col 60}{space 3}0.016{col 68}{space 4} 1.138927{col 81}{space 3} 3.566701
{txt}{space 23}19  {c |}{col 28}{res}{space 2} .7865134{col 40}{space 2} .1134181{col 51}{space 1}   -1.67{col 60}{space 3}0.096{col 68}{space 4} .5928704{col 81}{space 3} 1.043404
{txt}{space 23}20  {c |}{col 28}{res}{space 2} .3109688{col 40}{space 2} .0956504{col 51}{space 1}   -3.80{col 60}{space 3}0.000{col 68}{space 4} .1701755{col 81}{space 3} .5682463
{txt}{space 23}21  {c |}{col 28}{res}{space 2} .8395588{col 40}{space 2} .0964631{col 51}{space 1}   -1.52{col 60}{space 3}0.128{col 68}{space 4} .6702708{col 81}{space 3} 1.051603
{txt}{space 23}22  {c |}{col 28}{res}{space 2} .4844347{col 40}{space 2} .1673371{col 51}{space 1}   -2.10{col 60}{space 3}0.036{col 68}{space 4} .2461539{col 81}{space 3} .9533751
{txt}{space 23}23  {c |}{col 28}{res}{space 2} 1.101179{col 40}{space 2} .2872981{col 51}{space 1}    0.37{col 60}{space 3}0.712{col 68}{space 4} .6603573{col 81}{space 3} 1.836271
{txt}{space 23}24  {c |}{col 28}{res}{space 2} .3223595{col 40}{space 2} .1368343{col 51}{space 1}   -2.67{col 60}{space 3}0.008{col 68}{space 4} .1402893{col 81}{space 3} .7407237
{txt}{space 23}25  {c |}{col 28}{res}{space 2}  1.45171{col 40}{space 2} .2402648{col 51}{space 1}    2.25{col 60}{space 3}0.024{col 68}{space 4} 1.049548{col 81}{space 3} 2.007973
{txt}{space 23}26  {c |}{col 28}{res}{space 2} .8100944{col 40}{space 2} .1410504{col 51}{space 1}   -1.21{col 60}{space 3}0.226{col 68}{space 4} .5758745{col 81}{space 3} 1.139576
{txt}{space 23}27  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}28  {c |}{col 28}{res}{space 2} 1.296314{col 40}{space 2} .2160065{col 51}{space 1}    1.56{col 60}{space 3}0.119{col 68}{space 4} .9351335{col 81}{space 3} 1.796996
{txt}{space 23}29  {c |}{col 28}{res}{space 2} 3.455913{col 40}{space 2} 1.149684{col 51}{space 1}    3.73{col 60}{space 3}0.000{col 68}{space 4} 1.800496{col 81}{space 3} 6.633357
{txt}{space 23}30  {c |}{col 28}{res}{space 2} 1.490025{col 40}{space 2} .4471542{col 51}{space 1}    1.33{col 60}{space 3}0.184{col 68}{space 4} .8274642{col 81}{space 3} 2.683106
{txt}{space 23}50  {c |}{col 28}{res}{space 2} 1.963156{col 40}{space 2} .3900306{col 51}{space 1}    3.40{col 60}{space 3}0.001{col 68}{space 4}  1.32997{col 81}{space 3} 2.897794
{txt}{space 23}51  {c |}{col 28}{res}{space 2} 3.170232{col 40}{space 2} .8547286{col 51}{space 1}    4.28{col 60}{space 3}0.000{col 68}{space 4} 1.868952{col 81}{space 3} 5.377546
{txt}{space 23}52  {c |}{col 28}{res}{space 2} 1.525858{col 40}{space 2} .5291799{col 51}{space 1}    1.22{col 60}{space 3}0.223{col 68}{space 4} .7732336{col 81}{space 3} 3.011048
{txt}{space 23}53  {c |}{col 28}{res}{space 2} 1.505091{col 40}{space 2} .1632623{col 51}{space 1}    3.77{col 60}{space 3}0.000{col 68}{space 4}  1.21683{col 81}{space 3} 1.861639
{txt}{space 23}54  {c |}{col 28}{res}{space 2} 1.660778{col 40}{space 2} .3217494{col 51}{space 1}    2.62{col 60}{space 3}0.009{col 68}{space 4}  1.13607{col 81}{space 3} 2.427831
{txt}{space 23}55  {c |}{col 28}{res}{space 2}  1.17161{col 40}{space 2} .4401006{col 51}{space 1}    0.42{col 60}{space 3}0.673{col 68}{space 4} .5610992{col 81}{space 3} 2.446393
{txt}{space 23}56  {c |}{col 28}{res}{space 2} 1.100927{col 40}{space 2} .4399089{col 51}{space 1}    0.24{col 60}{space 3}0.810{col 68}{space 4} .5030777{col 81}{space 3} 2.409251
{txt}{space 23}57  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}58  {c |}{col 28}{res}{space 2}  1.25028{col 40}{space 2} .4749557{col 51}{space 1}    0.59{col 60}{space 3}0.557{col 68}{space 4}  .593818{col 81}{space 3} 2.632457
{txt}{space 23}59  {c |}{col 28}{res}{space 2} .3820146{col 40}{space 2} .0958744{col 51}{space 1}   -3.83{col 60}{space 3}0.000{col 68}{space 4} .2335896{col 81}{space 3} .6247501
{txt}{space 23}60  {c |}{col 28}{res}{space 2} .9488226{col 40}{space 2} .1392513{col 51}{space 1}   -0.36{col 60}{space 3}0.720{col 68}{space 4} .7116407{col 81}{space 3} 1.265055
{txt}{space 23}61  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 26} {c |}
{space 20}reagan {c |}{col 28}{res}{space 2} .0617049{col 40}{space 2} .0572943{col 51}{space 1}   -3.00{col 60}{space 3}0.003{col 68}{space 4} .0099991{col 81}{space 3} .3807841
{txt}{space 20}bush41 {c |}{col 28}{res}{space 2} .1551096{col 40}{space 2} .0936646{col 51}{space 1}   -3.09{col 60}{space 3}0.002{col 68}{space 4} .0474932{col 81}{space 3} .5065774
{txt}{space 19}clinton {c |}{col 28}{res}{space 2} .6340865{col 40}{space 2} .3300955{col 51}{space 1}   -0.88{col 60}{space 3}0.382{col 68}{space 4} .2285737{col 81}{space 3}  1.75902
{txt}{space 20}bush43 {c |}{col 28}{res}{space 2} .2181921{col 40}{space 2} .1564885{col 51}{space 1}   -2.12{col 60}{space 3}0.034{col 68}{space 4} .0534999{col 81}{space 3} .8898671
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0004364{col 40}{space 2} .0023087{col 51}{space 1}   -1.46{col 60}{space 3}0.144{col 68}{space 4} 1.37e-08{col 81}{space 3} 13.89843
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} .9886731{col 40}{space 2} .0303433{col 51}{space 1}   32.58{col 60}{space 3}0.000{col 68}{space 4} .9292014{col 81}{space 3} 1.048145
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 2.687666{col 40}{space 2} .0815526{col 68}{space 4} 2.532486{col 81}{space 3} 2.852355
{txt}                       1/p {c |}{col 28}{res}{space 2}   .37207{col 40}{space 2} .0112898{col 68}{space 4} .3505875{col 81}{space 3} .3948689
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimates store modelC4a
{txt}
{com}. 
. 
. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. 
. *Interquartile range
. 
. margins, predict(median time) at(loyalppdiffnom=(-0.3960373 0.9692858))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~m}{space 4}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~m}{space 4}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     5.29{col 38}{space 2}   0.0214
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 239.1565{col 26}{space 2} 103.9678{col 37}{space 5}  35.3833{col 51}{space 3} 442.9296
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelC4azloyalnom = r(table)
{txt}
{com}. mat list modelC4azloyalnom
{res}
{txt}modelC4azloyalnom[9,1]
            r2vs1.
              _at
     b {res} 239.15646
{txt}    se {res} 103.96781
{txt}     z {res} 2.3002933
{txt}pvalue {res} .02143161
{txt}    ll {res} 35.383296
{txt}    ul {res} 442.92962
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. *
. *
. 
. margins, predict(median time) at(loyalppdiffonoff=(-0.3960373 0.9692858))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~f}{space 4}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~f}{space 4}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     0.99{col 38}{space 2}   0.3201
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2}  77.2525{col 26}{space 2} 77.69774{col 37}{space 5}-75.03227{col 51}{space 3} 229.5373
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. matrix modelC4azloyalonoff = r(table)
{txt}
{com}. mat list modelC4azloyalonoff
{res}
{txt}modelC4azloyalonoff[9,1]
             r2vs1.
               _at
     b {res}  77.252498
{txt}    se {res}  77.697737
{txt}     z {res}  .99426961
{txt}pvalue {res}  .32009163
{txt}    ll {res} -75.032268
{txt}    ul {res}  229.53726
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. *
. *
. margins, predict(median time) at(loyalppdiffoffon=(-0.3960373 0.9692858))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~n}{space 4}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~n}{space 4}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     0.01{col 38}{space 2}   0.9371
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 7.290604{col 26}{space 2} 92.36894{col 37}{space 5}-173.7492{col 51}{space 3} 188.3304
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. matrix modelC4azloyaloffon = r(table)
{txt}
{com}. mat list modelC4azloyaloffon
{res}
{txt}modelC4azloyaloffon[9,1]
             r2vs1.
               _at
     b {res}  7.2906035
{txt}    se {res}   92.36894
{txt}     z {res}  .07892917
{txt}pvalue {res}  .93708896
{txt}    ll {res} -173.74919
{txt}    ul {res}   188.3304
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. *
. *
. *
. 
. *Interdecile range
. 
. estimates restore modelC4a
{txt}(results {stata estimates replay modelC4a:modelC4a} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiffnom=(-0.6451644 1.711348))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~m}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~m}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 888.1821{col 26}{space 2} 45.99473{col 37}{space 1}   19.31{col 46}{space 3}0.000{col 54}{space 4} 798.0341{col 67}{space 3} 978.3301
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1320.308{col 26}{space 2}   154.99{col 37}{space 1}    8.52{col 46}{space 3}0.000{col 54}{space 4} 1016.533{col 67}{space 3} 1624.083
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiffnom=(-0.6451644 1.711348))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~m}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~m}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     4.82{col 38}{space 2}   0.0282
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 432.1256{col 26}{space 2} 196.8664{col 37}{space 5} 46.27454{col 51}{space 3} 817.9767
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelC4bzloyalnom = r(table)
{txt}
{com}. mat list modelC4bzloyalnom
{res}
{txt}modelC4bzloyalnom[9,1]
            r2vs1.
              _at
     b {res} 432.12564
{txt}    se {res} 196.86642
{txt}     z {res} 2.1950195
{txt}pvalue {res}  .0281622
{txt}    ll {res} 46.274542
{txt}    ul {res} 817.97674
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. margins, predict(median time) at(loyalppdiffonoff=(-0.6451644 1.711348))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~f}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~f}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}  966.234{col 26}{space 2}  40.1058{col 37}{space 1}   24.09{col 46}{space 3}0.000{col 54}{space 4} 887.6281{col 67}{space 3}  1044.84
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1101.473{col 26}{space 2} 102.8624{col 37}{space 1}   10.71{col 46}{space 3}0.000{col 54}{space 4} 899.8668{col 67}{space 3}  1303.08
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiffonoff=(-0.6451644 1.711348))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~f}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~f}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     0.96{col 38}{space 2}   0.3270
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 135.2394{col 26}{space 2} 137.9837{col 37}{space 5}-135.2037{col 51}{space 3} 405.6826
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelC4bzloyalonoff = r(table)
{txt}
{com}. mat list modelC4bzloyalonoff
{res}
{txt}modelC4bzloyalonoff[9,1]
             r2vs1.
               _at
     b {res}  135.23945
{txt}    se {res}  137.98375
{txt}     z {res}  .98011143
{txt}pvalue {res}  .32703112
{txt}    ll {res} -135.20373
{txt}    ul {res}  405.68262
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. 
. margins, predict(median time) at(loyalppdiffoffon=(-0.6451644 1.711348))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~n}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~n}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 999.8683{col 26}{space 2} 48.55941{col 37}{space 1}   20.59{col 46}{space 3}0.000{col 54}{space 4} 904.6937{col 67}{space 3} 1095.043
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1012.468{col 26}{space 2}  115.449{col 37}{space 1}    8.77{col 46}{space 3}0.000{col 54}{space 4} 786.1925{col 67}{space 3} 1238.744
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiffoffon=(-0.6451644 1.711348))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~n}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~n}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     0.01{col 38}{space 2}   0.9372
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 12.59994{col 26}{space 2} 159.8462{col 37}{space 5}-300.6929{col 51}{space 3} 325.8928
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelC4bzloyaloffon = r(table)
{txt}
{com}. mat list modelC4bzloyaloffon
{res}
{txt}modelC4bzloyaloffon[9,1]
             r2vs1.
               _at
     b {res}   12.59994
{txt}    se {res}  159.84624
{txt}     z {res}  .07882538
{txt}pvalue {res}  .93717152
{txt}    ll {res} -300.69292
{txt}    ul {res}   325.8928
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. 
. *********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. **** ALTERNATIVE DATABASE ANALYSIS OF REPORTED MODELS [C1-C4]: MULTIPLE SPELLS/RECORDS DATA [COX & WEIBULL MODELS]  ***
. 
. 
. use "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Data\Krause and Byers.MRD.06-03-2022.dta", replace
{txt}
{com}. 
. 
. *
. *
. 
. 
. ** GENERATE CENSORING VARIABLE FOR HOLDOVER APPOINTEES SERVING BETWEEN/ACROSS ADMINISTRATIONS [=1]; UNCENSRED OBSERVATIONS [=0] ** 
. 
. gen singleadmin_service=1 if holdover==0
{txt}(28,710 missing values generated)

{com}. *
. replace singleadmin_service=0 if holdover==1
{txt}(28,710 real changes made)

{com}. *
. *
. tab singleadmin_service

{txt}singleadmin {c |}
   _service {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}     28,710        3.38        3.38
{txt}          1 {c |}{res}    821,324       96.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    850,034      100.00
{txt}
{com}. 
. 
. 
. ** SET DATABASE WITH MULTIPLE SPELLS FOR EACH APPOINTEE OBSERVATION [ID(OBSIDENT)]; "DEPART" IS A FAILURE BINARY VARIABLE: A SINGLE "1" PER APPOINTEE OBSERVATION, N = 860 [UNCENSORED N = 831; CENSORED N = 29] ** 
. stset okapptdur, failure(singleadmin_service)  id(obsident)

                {txt}id:  {res}obsident
     {txt}failure event:  {res}singleadmin_service != 0 & singleadmin_service < .
{txt}obs. time interval:  {res}(okapptdur[_n-1], okapptdur]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}    850,034{txt}  total observations
{res}    820,493{txt}  observations begin on or after (first) failure
{hline 78}
{res}     29,541{txt}  observations remaining, representing
{res}        860{txt}  subjects
{res}        831{txt}  failures in single-failure-per-subject data
{res}     29,541{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}    3,229
{txt}
{com}. 
. 
. 
. 
. 
. **** MODEL C5: COX MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2onpanel  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign abssenpartydiffmean absfilipresdistancey okcrossover avgpresapp  unemployment i. okstartadyr ,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
                 {txt}id:  {res}obsident

{txt}Iteration 0:   log pseudolikelihood = {res}-5615.0108
{txt}Iteration 1:   log pseudolikelihood = {res}  -5612.33
{txt}Iteration 2:   log pseudolikelihood = {res}-5612.3279
{txt}Iteration 3:   log pseudolikelihood = {res}-5612.3279
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-5612.3279

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}    29,541
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}       29541
                                                {txt}Wald chi2({res}22{txt})    =  {res}     62.85
{txt}Log pseudolikelihood =   {res}-5612.3279             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 104:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 39}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 40}{c |}{col 52}    Robust
{col 1}                                    _t{col 40}{c |} Haz. Ratio{col 52}   Std. Err.{col 64}      z{col 72}   P>|z|{col 80}     [95% Con{col 93}f. Interval]
{hline 39}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}zloyalmedian {c |}{col 40}{res}{space 2} 1.067089{col 52}{space 2} .0292682{col 63}{space 1}    2.37{col 72}{space 3}0.018{col 80}{space 4} 1.011239{col 93}{space 3} 1.126024
{txt}{space 13}1.soubinaryagency2onpanel {c |}{col 40}{res}{space 2} 1.030292{col 52}{space 2} .0172763{col 63}{space 1}    1.78{col 72}{space 3}0.075{col 80}{space 4} .9969815{col 93}{space 3} 1.064716
{txt}{space 38} {c |}
soubinaryagency2onpanel#c.zloyalmedian {c |}
{space 36}1  {c |}{col 40}{res}{space 2}  .943621{col 52}{space 2} .0233094{col 63}{space 1}   -2.35{col 72}{space 3}0.019{col 80}{space 4} .8990237{col 93}{space 3} .9904305
{txt}{space 38} {c |}
{space 25}zpecompmedian {c |}{col 40}{res}{space 2} .9880047{col 52}{space 2} .0092146{col 63}{space 1}   -1.29{col 72}{space 3}0.196{col 80}{space 4} .9701085{col 93}{space 3} 1.006231
{txt}{space 25}zmecompmedian {c |}{col 40}{res}{space 2} .9639699{col 52}{space 2} .0104737{col 63}{space 1}   -3.38{col 72}{space 3}0.001{col 80}{space 4} .9436589{col 93}{space 3}  .984718
{txt}{space 29}toplevel2 {c |}{col 40}{res}{space 2} .9897823{col 52}{space 2}  .011215{col 63}{space 1}   -0.91{col 72}{space 3}0.365{col 80}{space 4} .9680437{col 93}{space 3} 1.012009
{txt}{space 18}presagencyideolalign {c |}{col 40}{res}{space 2} .9979243{col 52}{space 2}  .015745{col 63}{space 1}   -0.13{col 72}{space 3}0.895{col 80}{space 4} .9675369{col 93}{space 3} 1.029266
{txt}{space 16}presagencyideolopposed {c |}{col 40}{res}{space 2} 1.000075{col 52}{space 2} .0176548{col 63}{space 1}    0.00{col 72}{space 3}0.997{col 80}{space 4}  .966064{col 93}{space 3} 1.035283
{txt}{space 23}subagencydesign {c |}{col 40}{res}{space 2}  .979298{col 52}{space 2} .0283461{col 63}{space 1}   -0.72{col 72}{space 3}0.470{col 80}{space 4} .9252873{col 93}{space 3} 1.036461
{txt}{space 16}standaloneagencydesign {c |}{col 40}{res}{space 2} .9545905{col 52}{space 2} .0228701{col 63}{space 1}   -1.94{col 72}{space 3}0.052{col 80}{space 4} .9108021{col 93}{space 3} 1.000484
{txt}{space 19}abssenpartydiffmean {c |}{col 40}{res}{space 2} 1.398641{col 52}{space 2} .3837245{col 63}{space 1}    1.22{col 72}{space 3}0.221{col 80}{space 4} .8169105{col 93}{space 3} 2.394627
{txt}{space 18}absfilipresdistancey {c |}{col 40}{res}{space 2} 1.020793{col 52}{space 2} .0483293{col 63}{space 1}    0.43{col 72}{space 3}0.664{col 80}{space 4} .9303317{col 93}{space 3} 1.120051
{txt}{space 27}okcrossover {c |}{col 40}{res}{space 2} 1.026968{col 52}{space 2} .0153914{col 63}{space 1}    1.78{col 72}{space 3}0.076{col 80}{space 4} .9972405{col 93}{space 3} 1.057582
{txt}{space 28}avgpresapp {c |}{col 40}{res}{space 2} 1.000112{col 52}{space 2} .0005104{col 63}{space 1}    0.22{col 72}{space 3}0.826{col 80}{space 4} .9991125{col 93}{space 3} 1.001113
{txt}{space 26}unemployment {c |}{col 40}{res}{space 2} 1.000869{col 52}{space 2} .0054907{col 63}{space 1}    0.16{col 72}{space 3}0.874{col 80}{space 4} .9901645{col 93}{space 3} 1.011688
{txt}{space 38} {c |}
{space 27}okstartadyr {c |}
{space 36}2  {c |}{col 40}{res}{space 2} 1.117219{col 52}{space 2} .0317453{col 63}{space 1}    3.90{col 72}{space 3}0.000{col 80}{space 4}   1.0567{col 93}{space 3} 1.181204
{txt}{space 36}3  {c |}{col 40}{res}{space 2} 1.103783{col 52}{space 2} .0251941{col 63}{space 1}    4.33{col 72}{space 3}0.000{col 80}{space 4} 1.055491{col 93}{space 3} 1.154284
{txt}{space 36}4  {c |}{col 40}{res}{space 2} 1.091805{col 52}{space 2} .0214308{col 63}{space 1}    4.47{col 72}{space 3}0.000{col 80}{space 4}   1.0506{col 93}{space 3} 1.134627
{txt}{space 36}5  {c |}{col 40}{res}{space 2} 1.094989{col 52}{space 2}   .03151{col 63}{space 1}    3.15{col 72}{space 3}0.002{col 80}{space 4}  1.03494{col 93}{space 3} 1.158522
{txt}{space 36}6  {c |}{col 40}{res}{space 2} 1.097154{col 52}{space 2} .0299479{col 63}{space 1}    3.40{col 72}{space 3}0.001{col 80}{space 4} 1.039999{col 93}{space 3} 1.157449
{txt}{space 36}7  {c |}{col 40}{res}{space 2} 1.097275{col 52}{space 2} .0269708{col 63}{space 1}    3.78{col 72}{space 3}0.000{col 80}{space 4} 1.045666{col 93}{space 3}  1.15143
{txt}{space 36}8  {c |}{col 40}{res}{space 2} 1.083592{col 52}{space 2}  .023707{col 63}{space 1}    3.67{col 72}{space 3}0.000{col 80}{space 4} 1.038109{col 93}{space 3} 1.131067
{txt}{hline 39}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}    29,541{col 28}-5615.011{col 39}-5612.328{col 50}    22{col 58} 11268.66{col 69} 11451.11
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. 
. *** COMPUTE Figure C1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}STANDALONE − NON-STANDALONE Difference{c )-} {c -(}{c -(}2 [M2 & M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQ = 1.3688693 [0.9781515 - (-0.3907178)]
. 
. lincomest 1.soubinaryagency2onpanel#c.zloyalmedian*1.3688693, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2onpanel#c.zloyalmedian*1.3688693

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .9236367{col 26}{space 2} .0312318{col 37}{space 1}   -2.35{col 46}{space 3}0.019{col 54}{space 4} .8644079{col 67}{space 3} .9869238
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC5zloyal = r(table)
{txt}
{com}. mat list modelC5zloyal
{res}
{txt}modelC5zloyal[9,1]
               (1)
     b {res}  .92363669
{txt}    se {res}  .03123177
{txt}     z {res} -2.3492244
{txt}pvalue {res}  .01881256
{txt}    ll {res}  .86440788
{txt}    ul {res}  .98692383
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. 
. **** MODEL C6: COX MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2onpanel  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign abssenpartydiffmean absfilipresdistancey okcrossover avgpresapp  unemployment i. okstartadyr i.sbagency reagan bush41 clinton bush43,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
                 {txt}id:  {res}obsident

{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
Iteration 0:   log pseudolikelihood = {res}-5615.0108
{txt}Iteration 1:   log pseudolikelihood = {res}-5611.3129
{txt}Iteration 2:   log pseudolikelihood = {res}-5611.2951
{txt}Iteration 3:   log pseudolikelihood = {res}-5611.2951
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-5611.2951

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}    29,541
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}       29541
                                                {txt}Wald chi2({res}23{txt})    =  {res}   1337.41
{txt}Log pseudolikelihood =   {res}-5611.2951             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 104:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 39}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 40}{c |}{col 52}    Robust
{col 1}                                    _t{col 40}{c |} Haz. Ratio{col 52}   Std. Err.{col 64}      z{col 72}   P>|z|{col 80}     [95% Con{col 93}f. Interval]
{hline 39}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}zloyalmedian {c |}{col 40}{res}{space 2} 1.064048{col 52}{space 2} .0302421{col 63}{space 1}    2.18{col 72}{space 3}0.029{col 80}{space 4} 1.006395{col 93}{space 3} 1.125003
{txt}{space 13}1.soubinaryagency2onpanel {c |}{col 40}{res}{space 2} 1.047314{col 52}{space 2} .0268291{col 63}{space 1}    1.80{col 72}{space 3}0.071{col 80}{space 4} .9960282{col 93}{space 3} 1.101241
{txt}{space 38} {c |}
soubinaryagency2onpanel#c.zloyalmedian {c |}
{space 36}1  {c |}{col 40}{res}{space 2} .9422943{col 52}{space 2} .0254959{col 63}{space 1}   -2.20{col 72}{space 3}0.028{col 80}{space 4} .8936252{col 93}{space 3} .9936141
{txt}{space 38} {c |}
{space 25}zpecompmedian {c |}{col 40}{res}{space 2} .9973926{col 52}{space 2} .0098796{col 63}{space 1}   -0.26{col 72}{space 3}0.792{col 80}{space 4} .9782157{col 93}{space 3} 1.016945
{txt}{space 25}zmecompmedian {c |}{col 40}{res}{space 2} .9601454{col 52}{space 2} .0102899{col 63}{space 1}   -3.79{col 72}{space 3}0.000{col 80}{space 4} .9401878{col 93}{space 3} .9805266
{txt}{space 29}toplevel2 {c |}{col 40}{res}{space 2} .9853396{col 52}{space 2} .0127161{col 63}{space 1}   -1.14{col 72}{space 3}0.252{col 80}{space 4}  .960729{col 93}{space 3} 1.010581
{txt}{space 18}presagencyideolalign {c |}{col 40}{res}{space 2} .8670166{col 52}{space 2} .0137421{col 63}{space 1}   -9.00{col 72}{space 3}0.000{col 80}{space 4} .8404967{col 93}{space 3} .8943733
{txt}{space 16}presagencyideolopposed {c |}{col 40}{res}{space 2} .8685054{col 52}{space 2} .0143194{col 63}{space 1}   -8.55{col 72}{space 3}0.000{col 80}{space 4} .8408885{col 93}{space 3} .8970293
{txt}{space 23}subagencydesign {c |}{col 40}{res}{space 2} 1.069814{col 52}{space 2}   .02503{col 63}{space 1}    2.88{col 72}{space 3}0.004{col 80}{space 4} 1.021864{col 93}{space 3} 1.120014
{txt}{space 16}standaloneagencydesign {c |}{col 40}{res}{space 2} 1.137208{col 52}{space 2} .0233452{col 63}{space 1}    6.26{col 72}{space 3}0.000{col 80}{space 4} 1.092361{col 93}{space 3} 1.183897
{txt}{space 19}abssenpartydiffmean {c |}{col 40}{res}{space 2} 3.408806{col 52}{space 2} 2.478999{col 63}{space 1}    1.69{col 72}{space 3}0.092{col 80}{space 4} .8195581{col 93}{space 3} 14.17832
{txt}{space 18}absfilipresdistancey {c |}{col 40}{res}{space 2} .7618504{col 52}{space 2} .0952428{col 63}{space 1}   -2.18{col 72}{space 3}0.030{col 80}{space 4} .5962888{col 93}{space 3} .9733807
{txt}{space 27}okcrossover {c |}{col 40}{res}{space 2}  1.02374{col 52}{space 2} .0206791{col 63}{space 1}    1.16{col 72}{space 3}0.245{col 80}{space 4} .9840017{col 93}{space 3} 1.065084
{txt}{space 28}avgpresapp {c |}{col 40}{res}{space 2} 1.000837{col 52}{space 2} .0006488{col 63}{space 1}    1.29{col 72}{space 3}0.197{col 80}{space 4}  .999566{col 93}{space 3} 1.002109
{txt}{space 26}unemployment {c |}{col 40}{res}{space 2} .9837114{col 52}{space 2} .0067732{col 63}{space 1}   -2.39{col 72}{space 3}0.017{col 80}{space 4} .9705254{col 93}{space 3} .9970765
{txt}{space 38} {c |}
{space 27}okstartadyr {c |}
{space 36}2  {c |}{col 40}{res}{space 2} 1.138691{col 52}{space 2} .0340933{col 63}{space 1}    4.34{col 72}{space 3}0.000{col 80}{space 4} 1.073792{col 93}{space 3} 1.207512
{txt}{space 36}3  {c |}{col 40}{res}{space 2} 1.135003{col 52}{space 2} .0320354{col 63}{space 1}    4.49{col 72}{space 3}0.000{col 80}{space 4}  1.07392{col 93}{space 3} 1.199561
{txt}{space 36}4  {c |}{col 40}{res}{space 2} 1.125086{col 52}{space 2} .0261909{col 63}{space 1}    5.06{col 72}{space 3}0.000{col 80}{space 4} 1.074907{col 93}{space 3} 1.177609
{txt}{space 36}5  {c |}{col 40}{res}{space 2}  1.06389{col 52}{space 2} .0232897{col 63}{space 1}    2.83{col 72}{space 3}0.005{col 80}{space 4} 1.019209{col 93}{space 3} 1.110531
{txt}{space 36}6  {c |}{col 40}{res}{space 2} 1.065342{col 52}{space 2} .0227536{col 63}{space 1}    2.96{col 72}{space 3}0.003{col 80}{space 4} 1.021667{col 93}{space 3} 1.110885
{txt}{space 36}7  {c |}{col 40}{res}{space 2} 1.080282{col 52}{space 2} .0231818{col 63}{space 1}    3.60{col 72}{space 3}0.000{col 80}{space 4} 1.035789{col 93}{space 3} 1.126687
{txt}{space 36}8  {c |}{col 40}{res}{space 2} 1.065144{col 52}{space 2} .0226953{col 63}{space 1}    2.96{col 72}{space 3}0.003{col 80}{space 4} 1.021578{col 93}{space 3} 1.110568
{txt}{space 38} {c |}
{space 30}sbagency {c |}
{space 36}2  {c |}{col 40}{res}{space 2} 1.122917{col 52}{space 2} .0168303{col 63}{space 1}    7.73{col 72}{space 3}0.000{col 80}{space 4}  1.09041{col 93}{space 3} 1.156393
{txt}{space 36}3  {c |}{col 40}{res}{space 2} 1.102134{col 52}{space 2} .0187299{col 63}{space 1}    5.72{col 72}{space 3}0.000{col 80}{space 4} 1.066029{col 93}{space 3} 1.139462
{txt}{space 36}4  {c |}{col 40}{res}{space 2}  1.01292{col 52}{space 2} .0198964{col 63}{space 1}    0.65{col 72}{space 3}0.513{col 80}{space 4}  .974665{col 93}{space 3} 1.052677
{txt}{space 36}5  {c |}{col 40}{res}{space 2}  1.15215{col 52}{space 2} .0172358{col 63}{space 1}    9.47{col 72}{space 3}0.000{col 80}{space 4} 1.118859{col 93}{space 3} 1.186432
{txt}{space 36}6  {c |}{col 40}{res}{space 2} 1.151561{col 52}{space 2} .0183307{col 63}{space 1}    8.87{col 72}{space 3}0.000{col 80}{space 4} 1.116188{col 93}{space 3} 1.188054
{txt}{space 36}7  {c |}{col 40}{res}{space 2} 1.108527{col 52}{space 2} .0190748{col 63}{space 1}    5.99{col 72}{space 3}0.000{col 80}{space 4} 1.071764{col 93}{space 3} 1.146551
{txt}{space 36}8  {c |}{col 40}{res}{space 2} 1.191492{col 52}{space 2} .0213262{col 63}{space 1}    9.79{col 72}{space 3}0.000{col 80}{space 4} 1.150418{col 93}{space 3} 1.234033
{txt}{space 36}9  {c |}{col 40}{res}{space 2} 1.152403{col 52}{space 2} .0180502{col 63}{space 1}    9.06{col 72}{space 3}0.000{col 80}{space 4} 1.117562{col 93}{space 3} 1.188329
{txt}{space 35}11  {c |}{col 40}{res}{space 2} 1.148432{col 52}{space 2}  .018962{col 63}{space 1}    8.38{col 72}{space 3}0.000{col 80}{space 4} 1.111862{col 93}{space 3} 1.186205
{txt}{space 35}12  {c |}{col 40}{res}{space 2} 1.138642{col 52}{space 2} .0176444{col 63}{space 1}    8.38{col 72}{space 3}0.000{col 80}{space 4}  1.10458{col 93}{space 3} 1.173755
{txt}{space 35}13  {c |}{col 40}{res}{space 2} 1.176712{col 52}{space 2} .0208969{col 63}{space 1}    9.16{col 72}{space 3}0.000{col 80}{space 4}  1.13646{col 93}{space 3} 1.218391
{txt}{space 35}14  {c |}{col 40}{res}{space 2} 1.141055{col 52}{space 2}  .017154{col 63}{space 1}    8.78{col 72}{space 3}0.000{col 80}{space 4} 1.107924{col 93}{space 3} 1.175176
{txt}{space 35}15  {c |}{col 40}{res}{space 2} 1.136893{col 52}{space 2} .0160104{col 63}{space 1}    9.11{col 72}{space 3}0.000{col 80}{space 4} 1.105943{col 93}{space 3}  1.16871
{txt}{space 35}16  {c |}{col 40}{res}{space 2} 1.004828{col 52}{space 2} .0140516{col 63}{space 1}    0.34{col 72}{space 3}0.731{col 80}{space 4} .9776616{col 93}{space 3}  1.03275
{txt}{space 35}17  {c |}{col 40}{res}{space 2} 1.007205{col 52}{space 2} .0121311{col 63}{space 1}    0.60{col 72}{space 3}0.551{col 80}{space 4} .9837064{col 93}{space 3} 1.031264
{txt}{space 35}18  {c |}{col 40}{res}{space 2} 1.125018{col 52}{space 2} .0150214{col 63}{space 1}    8.82{col 72}{space 3}0.000{col 80}{space 4} 1.095958{col 93}{space 3} 1.154848
{txt}{space 35}19  {c |}{col 40}{res}{space 2}  .986325{col 52}{space 2} .0123778{col 63}{space 1}   -1.10{col 72}{space 3}0.273{col 80}{space 4} .9623609{col 93}{space 3} 1.010886
{txt}{space 35}20  {c |}{col 40}{res}{space 2} .7511891{col 52}{space 2} .0120749{col 63}{space 1}  -17.80{col 72}{space 3}0.000{col 80}{space 4} .7278917{col 93}{space 3} .7752323
{txt}{space 35}21  {c |}{col 40}{res}{space 2} 1.044881{col 52}{space 2} .0096804{col 63}{space 1}    4.74{col 72}{space 3}0.000{col 80}{space 4} 1.026079{col 93}{space 3} 1.064027
{txt}{space 35}22  {c |}{col 40}{res}{space 2} .8791511{col 52}{space 2} .0160521{col 63}{space 1}   -7.05{col 72}{space 3}0.000{col 80}{space 4} .8482458{col 93}{space 3} .9111823
{txt}{space 35}23  {c |}{col 40}{res}{space 2} .8974126{col 52}{space 2} .0136542{col 63}{space 1}   -7.11{col 72}{space 3}0.000{col 80}{space 4}  .871046{col 93}{space 3} .9245773
{txt}{space 35}24  {c |}{col 40}{res}{space 2} .7802022{col 52}{space 2} .0157347{col 63}{space 1}  -12.31{col 72}{space 3}0.000{col 80}{space 4} .7499643{col 93}{space 3} .8116594
{txt}{space 35}25  {c |}{col 40}{res}{space 2} .9684986{col 52}{space 2} .0203246{col 63}{space 1}   -1.53{col 72}{space 3}0.127{col 80}{space 4} .9294713{col 93}{space 3} 1.009165
{txt}{space 35}26  {c |}{col 40}{res}{space 2} .9405833{col 52}{space 2} .0092189{col 63}{space 1}   -6.25{col 72}{space 3}0.000{col 80}{space 4} .9226871{col 93}{space 3} .9588266
{txt}{space 35}27  {c |}{col 40}{res}{space 2}        1{col 52}{txt}  (omitted)
{space 35}28  {c |}{col 40}{res}{space 2} 1.061933{col 52}{space 2} .0174582{col 63}{space 1}    3.66{col 72}{space 3}0.000{col 80}{space 4}  1.02826{col 93}{space 3} 1.096707
{txt}{space 35}29  {c |}{col 40}{res}{space 2} 1.155967{col 52}{space 2} .0244892{col 63}{space 1}    6.84{col 72}{space 3}0.000{col 80}{space 4} 1.108952{col 93}{space 3} 1.204975
{txt}{space 35}30  {c |}{col 40}{res}{space 2} 1.164474{col 52}{space 2} .0248368{col 63}{space 1}    7.14{col 72}{space 3}0.000{col 80}{space 4} 1.116799{col 93}{space 3} 1.214185
{txt}{space 35}50  {c |}{col 40}{res}{space 2} 1.088334{col 52}{space 2}  .019264{col 63}{space 1}    4.78{col 72}{space 3}0.000{col 80}{space 4} 1.051225{col 93}{space 3} 1.126753
{txt}{space 35}51  {c |}{col 40}{res}{space 2} 1.089348{col 52}{space 2} .0264502{col 63}{space 1}    3.52{col 72}{space 3}0.000{col 80}{space 4} 1.038721{col 93}{space 3} 1.142443
{txt}{space 35}52  {c |}{col 40}{res}{space 2} .7995075{col 52}{space 2} .0133602{col 63}{space 1}  -13.39{col 72}{space 3}0.000{col 80}{space 4} .7737461{col 93}{space 3} .8261266
{txt}{space 35}53  {c |}{col 40}{res}{space 2} .9997692{col 52}{space 2} .0050512{col 63}{space 1}   -0.05{col 72}{space 3}0.964{col 80}{space 4} .9899177{col 93}{space 3} 1.009719
{txt}{space 35}54  {c |}{col 40}{res}{space 2}  .918573{col 52}{space 2} .0119644{col 63}{space 1}   -6.52{col 72}{space 3}0.000{col 80}{space 4}   .89542{col 93}{space 3} .9423247
{txt}{space 35}55  {c |}{col 40}{res}{space 2} 1.052506{col 52}{space 2} .0312981{col 63}{space 1}    1.72{col 72}{space 3}0.085{col 80}{space 4} .9929167{col 93}{space 3} 1.115672
{txt}{space 35}56  {c |}{col 40}{res}{space 2} 1.089841{col 52}{space 2} .0333378{col 63}{space 1}    2.81{col 72}{space 3}0.005{col 80}{space 4}  1.02642{col 93}{space 3}  1.15718
{txt}{space 35}57  {c |}{col 40}{res}{space 2}        1{col 52}{txt}  (omitted)
{space 35}58  {c |}{col 40}{res}{space 2} .9509251{col 52}{space 2} .0285477{col 63}{space 1}   -1.68{col 72}{space 3}0.094{col 80}{space 4}  .896587{col 93}{space 3} 1.008556
{txt}{space 35}59  {c |}{col 40}{res}{space 2} 1.009045{col 52}{space 2} .0199991{col 63}{space 1}    0.45{col 72}{space 3}0.650{col 80}{space 4} .9705993{col 93}{space 3} 1.049014
{txt}{space 35}60  {c |}{col 40}{res}{space 2} 1.020506{col 52}{space 2} .0144392{col 63}{space 1}    1.43{col 72}{space 3}0.151{col 80}{space 4} .9925949{col 93}{space 3} 1.049203
{txt}{space 35}61  {c |}{col 40}{res}{space 2}        1{col 52}{txt}  (omitted)
{space 38} {c |}
{space 32}reagan {c |}{col 40}{res}{space 2} 1.156126{col 52}{space 2} .0578937{col 63}{space 1}    2.90{col 72}{space 3}0.004{col 80}{space 4} 1.048047{col 93}{space 3} 1.275351
{txt}{space 32}bush41 {c |}{col 40}{res}{space 2}  1.03513{col 52}{space 2} .0346739{col 63}{space 1}    1.03{col 72}{space 3}0.303{col 80}{space 4} .9693528{col 93}{space 3}  1.10537
{txt}{space 31}clinton {c |}{col 40}{res}{space 2} 1.007559{col 52}{space 2} .0501614{col 63}{space 1}    0.15{col 72}{space 3}0.880{col 80}{space 4} .9138886{col 93}{space 3}  1.11083
{txt}{space 32}bush43 {c |}{col 40}{res}{space 2} 1.052925{col 52}{space 2} .0629759{col 63}{space 1}    0.86{col 72}{space 3}0.389{col 80}{space 4} .9364541{col 93}{space 3} 1.183881
{txt}{hline 39}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}    29,541{col 28}-5615.011{col 39}-5611.295{col 50}    23{col 58} 11268.59{col 69} 11459.34
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. 
. *** COMPUTE Figure C1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}STANDALONE − NON-STANDALONE Difference{c )-} {c -(}{c -(}2 [M2 & M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQ = 1.3688693 [0.9781515 - (-0.3907178)]
. 
. lincomest 1.soubinaryagency2onpanel#c.zloyalmedian*1.3688693, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2onpanel#c.zloyalmedian*1.3688693

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .9218596{col 26}{space 2} .0341437{col 37}{space 1}   -2.20{col 46}{space 3}0.028{col 54}{space 4} .8573105{col 67}{space 3} .9912689
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC6zloyal = r(table)
{txt}
{com}. mat list modelC6zloyal
{res}
{txt}modelC6zloyal[9,1]
              (1)
     b {res} .92185962
{txt}    se {res} .03414369
{txt}     z {res} -2.196735
{txt}pvalue {res} .02803938
{txt}    ll {res} .85731047
{txt}    ul {res} .99126885
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         1
{reset}
{com}. 
. 
. 
. 
. 
. 
. **** MODEL C7: WEIBULL MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg  c.zloyalmedian##i.soubinaryagency2onpanel  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign abssenpartydiffmean absfilipresdistancey okcrossover avgpresapp  unemployment i. okstartadyr, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
                 {txt}id:  {res}obsident

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-3798.4217
{txt}Iteration 1:   log pseudolikelihood = {res}-2630.2658
{txt}Iteration 2:   log pseudolikelihood = {res}-2017.4921
{txt}Iteration 3:   log pseudolikelihood = {res} -1936.816
{txt}Iteration 4:   log pseudolikelihood = {res}-1935.8198
{txt}Iteration 5:   log pseudolikelihood = {res}-1935.8196

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1935.8196}  
Iteration 1:{space 3}log pseudolikelihood = {res: -1679.699}  
Iteration 2:{space 3}log pseudolikelihood = {res: -1672.516}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1672.5026}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1672.5026}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}    29,541
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}       29541
                                                {txt}Wald chi2({res}22{txt})    =  {res}    757.41
{txt}Log pseudolikelihood =   {res}-1672.5026             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 104:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 39}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 40}{c |}{col 52}    Robust
{col 1}                                    _t{col 40}{c |} Haz. Ratio{col 52}   Std. Err.{col 64}      z{col 72}   P>|z|{col 80}     [95% Con{col 93}f. Interval]
{hline 39}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}zloyalmedian {c |}{col 40}{res}{space 2}  1.57527{col 52}{space 2} .1854022{col 63}{space 1}    3.86{col 72}{space 3}0.000{col 80}{space 4} 1.250755{col 93}{space 3} 1.983981
{txt}{space 13}1.soubinaryagency2onpanel {c |}{col 40}{res}{space 2} 1.218444{col 52}{space 2} .1092423{col 63}{space 1}    2.20{col 72}{space 3}0.028{col 80}{space 4}  1.02209{col 93}{space 3} 1.452519
{txt}{space 38} {c |}
soubinaryagency2onpanel#c.zloyalmedian {c |}
{space 36}1  {c |}{col 40}{res}{space 2} .7015231{col 52}{space 2} .0708085{col 63}{space 1}   -3.51{col 72}{space 3}0.000{col 80}{space 4} .5756065{col 93}{space 3} .8549846
{txt}{space 38} {c |}
{space 25}zpecompmedian {c |}{col 40}{res}{space 2} .8673284{col 52}{space 2} .0729469{col 63}{space 1}   -1.69{col 72}{space 3}0.091{col 80}{space 4} .7355175{col 93}{space 3} 1.022761
{txt}{space 25}zmecompmedian {c |}{col 40}{res}{space 2} .6846054{col 52}{space 2} .0394961{col 63}{space 1}   -6.57{col 72}{space 3}0.000{col 80}{space 4} .6114107{col 93}{space 3} .7665627
{txt}{space 29}toplevel2 {c |}{col 40}{res}{space 2}  .810178{col 52}{space 2} .0972386{col 63}{space 1}   -1.75{col 72}{space 3}0.079{col 80}{space 4}  .640351{col 93}{space 3} 1.025045
{txt}{space 18}presagencyideolalign {c |}{col 40}{res}{space 2} .9307402{col 52}{space 2}  .127454{col 63}{space 1}   -0.52{col 72}{space 3}0.600{col 80}{space 4} .7116498{col 93}{space 3}  1.21728
{txt}{space 16}presagencyideolopposed {c |}{col 40}{res}{space 2} .9605401{col 52}{space 2} .1341485{col 63}{space 1}   -0.29{col 72}{space 3}0.773{col 80}{space 4} .7305285{col 93}{space 3} 1.262972
{txt}{space 23}subagencydesign {c |}{col 40}{res}{space 2} .8841526{col 52}{space 2} .1990251{col 63}{space 1}   -0.55{col 72}{space 3}0.584{col 80}{space 4} .5687476{col 93}{space 3} 1.374469
{txt}{space 16}standaloneagencydesign {c |}{col 40}{res}{space 2} .7002747{col 52}{space 2} .1296666{col 63}{space 1}   -1.92{col 72}{space 3}0.054{col 80}{space 4} .4871419{col 93}{space 3} 1.006657
{txt}{space 19}abssenpartydiffmean {c |}{col 40}{res}{space 2} 243.1239{col 52}{space 2} 536.4409{col 63}{space 1}    2.49{col 72}{space 3}0.013{col 80}{space 4} 3.218797{col 93}{space 3} 18363.77
{txt}{space 18}absfilipresdistancey {c |}{col 40}{res}{space 2} .8739452{col 52}{space 2} .3113713{col 63}{space 1}   -0.38{col 72}{space 3}0.705{col 80}{space 4} .4347265{col 93}{space 3} 1.756921
{txt}{space 27}okcrossover {c |}{col 40}{res}{space 2} 1.286684{col 52}{space 2} .1579884{col 63}{space 1}    2.05{col 72}{space 3}0.040{col 80}{space 4} 1.011475{col 93}{space 3} 1.636773
{txt}{space 28}avgpresapp {c |}{col 40}{res}{space 2} 1.003401{col 52}{space 2} .0033705{col 63}{space 1}    1.01{col 72}{space 3}0.312{col 80}{space 4} .9968165{col 93}{space 3} 1.010029
{txt}{space 26}unemployment {c |}{col 40}{res}{space 2} 1.063645{col 52}{space 2} .0408783{col 63}{space 1}    1.61{col 72}{space 3}0.108{col 80}{space 4} .9864685{col 93}{space 3}  1.14686
{txt}{space 38} {c |}
{space 27}okstartadyr {c |}
{space 36}2  {c |}{col 40}{res}{space 2}  3.27256{col 52}{space 2} .4023846{col 63}{space 1}    9.64{col 72}{space 3}0.000{col 80}{space 4} 2.571735{col 93}{space 3} 4.164366
{txt}{space 36}3  {c |}{col 40}{res}{space 2} 2.830573{col 52}{space 2} .3102181{col 63}{space 1}    9.49{col 72}{space 3}0.000{col 80}{space 4} 2.283423{col 93}{space 3} 3.508829
{txt}{space 36}4  {c |}{col 40}{res}{space 2}  2.86451{col 52}{space 2} .3135904{col 63}{space 1}    9.61{col 72}{space 3}0.000{col 80}{space 4} 2.311349{col 93}{space 3} 3.550055
{txt}{space 36}5  {c |}{col 40}{res}{space 2} 2.840114{col 52}{space 2} .4313982{col 63}{space 1}    6.87{col 72}{space 3}0.000{col 80}{space 4} 2.108836{col 93}{space 3} 3.824977
{txt}{space 36}6  {c |}{col 40}{res}{space 2} 3.019684{col 52}{space 2} .4496095{col 63}{space 1}    7.42{col 72}{space 3}0.000{col 80}{space 4}   2.2554{col 93}{space 3} 4.042959
{txt}{space 36}7  {c |}{col 40}{res}{space 2} 3.309632{col 52}{space 2} .4505361{col 63}{space 1}    8.79{col 72}{space 3}0.000{col 80}{space 4} 2.534584{col 93}{space 3} 4.321681
{txt}{space 36}8  {c |}{col 40}{res}{space 2} 3.072689{col 52}{space 2} .4537001{col 63}{space 1}    7.60{col 72}{space 3}0.000{col 80}{space 4} 2.300561{col 93}{space 3} 4.103962
{txt}{space 38} {c |}
{space 33}_cons {c |}{col 40}{res}{space 2} .0055951{col 52}{space 2} .0081406{col 63}{space 1}   -3.56{col 72}{space 3}0.000{col 80}{space 4} .0003231{col 93}{space 3}  .096886
{txt}{hline 39}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 33}/ln_p {c |}{col 40}{res}{space 2}-.4895306{col 52}{space 2} .0361873{col 63}{space 1}  -13.53{col 72}{space 3}0.000{col 80}{space 4}-.5604563{col 93}{space 3}-.4186049
{txt}{hline 39}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                     p {c |}{col 40}{res}{space 2}  .612914{col 52}{space 2} .0221797{col 80}{space 4} .5709485{col 93}{space 3} .6579641
{txt}                                   1/p {c |}{col 40}{res}{space 2}  1.63155{col 52}{space 2} .0590413{col 80}{space 4}  1.51984{col 93}{space 3} 1.751472
{txt}{hline 39}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}    29,541{col 28} -1935.82{col 39}-1672.503{col 50}    24{col 58} 3393.005{col 69}  3592.05
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. 
. 
. *** COMPUTE Figure C1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}STANDALONE − NON-STANDALONE Difference{c )-} {c -(}{c -(}2 [M2 & M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE:  IQ = 1.3688693 [0.9781515 - (-0.3907178)]
. 
. lincomest 1.soubinaryagency2onpanel#c.zloyalmedian*1.3688693, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2onpanel#c.zloyalmedian*1.3688693

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .6155334{col 26}{space 2} .0850466{col 37}{space 1}   -3.51{col 46}{space 3}0.000{col 54}{space 4} .4695085{col 67}{space 3} .8069744
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC7zloyal = r(table)
{txt}
{com}. mat list modelC7zloyal
{res}
{txt}modelC7zloyal[9,1]
               (1)
     b {res}  .61553336
{txt}    se {res}  .08504655
{txt}     z {res} -3.5121646
{txt}pvalue {res}  .00044447
{txt}    ll {res}  .46950845
{txt}    ul {res}  .80697443
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. **** COMPUTE Figure C2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. **  NOTE: IQ = 1.3688693 [0.9781515 - (-0.3907178)] **
. 
. *drop loyalppdiff
. generate loyalppdiffonpanel = soubinaryagency2onpanel*zloyalmedian
{txt}
{com}. 
. ** Re-Estimate Model C7  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2onpanel loyalppdiffonpanel  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign abssenpartydiffmean absfilipresdistancey okcrossover avgpresapp  unemployment i. okstartadyr, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
                 {txt}id:  {res}obsident

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-3798.4217
{txt}Iteration 1:   log pseudolikelihood = {res}-2630.2658
{txt}Iteration 2:   log pseudolikelihood = {res}-2017.4921
{txt}Iteration 3:   log pseudolikelihood = {res} -1936.816
{txt}Iteration 4:   log pseudolikelihood = {res}-1935.8198
{txt}Iteration 5:   log pseudolikelihood = {res}-1935.8196

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1935.8196}  
Iteration 1:{space 3}log pseudolikelihood = {res: -1679.699}  
Iteration 2:{space 3}log pseudolikelihood = {res: -1672.516}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1672.5026}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1672.5026}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}    29,541
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}       29541
                                                {txt}Wald chi2({res}22{txt})    =  {res}    757.41
{txt}Log pseudolikelihood =   {res}-1672.5026             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 89:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                     _t{col 25}{c |} Haz. Ratio{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}zloyalmedian {c |}{col 25}{res}{space 2}  1.57527{col 37}{space 2} .1854022{col 48}{space 1}    3.86{col 57}{space 3}0.000{col 65}{space 4} 1.250755{col 78}{space 3} 1.983981
{txt}soubinaryagency2onpanel {c |}{col 25}{res}{space 2} 1.218444{col 37}{space 2} .1092423{col 48}{space 1}    2.20{col 57}{space 3}0.028{col 65}{space 4}  1.02209{col 78}{space 3} 1.452519
{txt}{space 5}loyalppdiffonpanel {c |}{col 25}{res}{space 2} .7015231{col 37}{space 2} .0708085{col 48}{space 1}   -3.51{col 57}{space 3}0.000{col 65}{space 4} .5756065{col 78}{space 3} .8549846
{txt}{space 10}zpecompmedian {c |}{col 25}{res}{space 2} .8673284{col 37}{space 2} .0729469{col 48}{space 1}   -1.69{col 57}{space 3}0.091{col 65}{space 4} .7355175{col 78}{space 3} 1.022761
{txt}{space 10}zmecompmedian {c |}{col 25}{res}{space 2} .6846054{col 37}{space 2} .0394961{col 48}{space 1}   -6.57{col 57}{space 3}0.000{col 65}{space 4} .6114107{col 78}{space 3} .7665627
{txt}{space 14}toplevel2 {c |}{col 25}{res}{space 2}  .810178{col 37}{space 2} .0972386{col 48}{space 1}   -1.75{col 57}{space 3}0.079{col 65}{space 4}  .640351{col 78}{space 3} 1.025045
{txt}{space 3}presagencyideolalign {c |}{col 25}{res}{space 2} .9307402{col 37}{space 2}  .127454{col 48}{space 1}   -0.52{col 57}{space 3}0.600{col 65}{space 4} .7116498{col 78}{space 3}  1.21728
{txt}{space 1}presagencyideolopposed {c |}{col 25}{res}{space 2} .9605401{col 37}{space 2} .1341485{col 48}{space 1}   -0.29{col 57}{space 3}0.773{col 65}{space 4} .7305285{col 78}{space 3} 1.262972
{txt}{space 8}subagencydesign {c |}{col 25}{res}{space 2} .8841526{col 37}{space 2} .1990251{col 48}{space 1}   -0.55{col 57}{space 3}0.584{col 65}{space 4} .5687476{col 78}{space 3} 1.374469
{txt}{space 1}standaloneagencydesign {c |}{col 25}{res}{space 2} .7002747{col 37}{space 2} .1296666{col 48}{space 1}   -1.92{col 57}{space 3}0.054{col 65}{space 4} .4871419{col 78}{space 3} 1.006657
{txt}{space 4}abssenpartydiffmean {c |}{col 25}{res}{space 2} 243.1239{col 37}{space 2} 536.4409{col 48}{space 1}    2.49{col 57}{space 3}0.013{col 65}{space 4} 3.218797{col 78}{space 3} 18363.77
{txt}{space 3}absfilipresdistancey {c |}{col 25}{res}{space 2} .8739452{col 37}{space 2} .3113713{col 48}{space 1}   -0.38{col 57}{space 3}0.705{col 65}{space 4} .4347265{col 78}{space 3} 1.756921
{txt}{space 12}okcrossover {c |}{col 25}{res}{space 2} 1.286684{col 37}{space 2} .1579884{col 48}{space 1}    2.05{col 57}{space 3}0.040{col 65}{space 4} 1.011475{col 78}{space 3} 1.636773
{txt}{space 13}avgpresapp {c |}{col 25}{res}{space 2} 1.003401{col 37}{space 2} .0033705{col 48}{space 1}    1.01{col 57}{space 3}0.312{col 65}{space 4} .9968165{col 78}{space 3} 1.010029
{txt}{space 11}unemployment {c |}{col 25}{res}{space 2} 1.063645{col 37}{space 2} .0408783{col 48}{space 1}    1.61{col 57}{space 3}0.108{col 65}{space 4} .9864685{col 78}{space 3}  1.14686
{txt}{space 23} {c |}
{space 12}okstartadyr {c |}
{space 21}2  {c |}{col 25}{res}{space 2}  3.27256{col 37}{space 2} .4023846{col 48}{space 1}    9.64{col 57}{space 3}0.000{col 65}{space 4} 2.571735{col 78}{space 3} 4.164366
{txt}{space 21}3  {c |}{col 25}{res}{space 2} 2.830573{col 37}{space 2} .3102181{col 48}{space 1}    9.49{col 57}{space 3}0.000{col 65}{space 4} 2.283423{col 78}{space 3} 3.508829
{txt}{space 21}4  {c |}{col 25}{res}{space 2}  2.86451{col 37}{space 2} .3135904{col 48}{space 1}    9.61{col 57}{space 3}0.000{col 65}{space 4} 2.311349{col 78}{space 3} 3.550055
{txt}{space 21}5  {c |}{col 25}{res}{space 2} 2.840114{col 37}{space 2} .4313982{col 48}{space 1}    6.87{col 57}{space 3}0.000{col 65}{space 4} 2.108836{col 78}{space 3} 3.824977
{txt}{space 21}6  {c |}{col 25}{res}{space 2} 3.019684{col 37}{space 2} .4496095{col 48}{space 1}    7.42{col 57}{space 3}0.000{col 65}{space 4}   2.2554{col 78}{space 3} 4.042959
{txt}{space 21}7  {c |}{col 25}{res}{space 2} 3.309632{col 37}{space 2} .4505361{col 48}{space 1}    8.79{col 57}{space 3}0.000{col 65}{space 4} 2.534584{col 78}{space 3} 4.321681
{txt}{space 21}8  {c |}{col 25}{res}{space 2} 3.072689{col 37}{space 2} .4537001{col 48}{space 1}    7.60{col 57}{space 3}0.000{col 65}{space 4} 2.300561{col 78}{space 3} 4.103962
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .0055951{col 37}{space 2} .0081406{col 48}{space 1}   -3.56{col 57}{space 3}0.000{col 65}{space 4} .0003231{col 78}{space 3}  .096886
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}/ln_p {c |}{col 25}{res}{space 2}-.4895306{col 37}{space 2} .0361873{col 48}{space 1}  -13.53{col 57}{space 3}0.000{col 65}{space 4}-.5604563{col 78}{space 3}-.4186049
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                      p {c |}{col 25}{res}{space 2}  .612914{col 37}{space 2} .0221797{col 65}{space 4} .5709485{col 78}{space 3} .6579641
{txt}                    1/p {c |}{col 25}{res}{space 2}  1.63155{col 37}{space 2} .0590413{col 65}{space 4}  1.51984{col 78}{space 3} 1.751472
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimates store modelc71
{txt}
{com}. 
. *margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9710589))
. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. 
. margins, predict(median time) at(loyalppdiffonpanel=(-0.3960373 0.9710589))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}    29,541
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~l}{space 4}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~l}{space 4}{txt:=} {space 3}.9710589}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     2.71{col 38}{space 2}   0.1000
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 35.01294{col 26}{space 2} 21.28588{col 37}{space 5} -6.70661{col 51}{space 3}  76.7325
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 9}{help j_multipredictwarn##|_new:Warning: Multiple }{help j_multipredictwarn##|_new:observations per subject are detected.  }{help j_multipredictwarn##|_new:Predictions that require averaging over }{help j_multipredictwarn##|_new:the dataset may not be appropriate.  }{help j_multipredictwarn##|_new:Use the {bf:at()} option to compute }{help j_multipredictwarn##|_new:predictions at fixed values of the }{help j_multipredictwarn##|_new:covariates.}{p_end}
{res}{txt}
{com}. 
. matrix modelC71azloyal = r(table)
{txt}
{com}. mat list modelC71azloyal
{res}
{txt}modelC71azloyal[9,1]
             r2vs1.
               _at
     b {res}  35.012943
{txt}    se {res}  21.285877
{txt}     z {res}  1.6448908
{txt}pvalue {res}  .09999234
{txt}    ll {res} -6.7066098
{txt}    ul {res}  76.732496
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. 
. 
. estimates restore modelc71
{txt}(results {stata estimates replay modelc71:modelc71} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiffonpanel=(-.6531436 1.756563))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}    29,541
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~l}{space 4}{txt:=} {space 2}-.6531436}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~l}{space 4}{txt:=} {space 3}1.756563}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}  25.0422{col 26}{space 2} 7.449551{col 37}{space 1}    3.36{col 46}{space 3}0.001{col 54}{space 4} 10.44135{col 67}{space 3} 39.64305
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 100.9177{col 26}{space 2} 56.17577{col 37}{space 1}    1.80{col 46}{space 3}0.072{col 54}{space 4}-9.184817{col 67}{space 3} 211.0202
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 9}{help j_multipredictwarn##|_new:Warning: Multiple }{help j_multipredictwarn##|_new:observations per subject are detected.  }{help j_multipredictwarn##|_new:Predictions that require averaging over }{help j_multipredictwarn##|_new:the dataset may not be appropriate.  }{help j_multipredictwarn##|_new:Use the {bf:at()} option to compute }{help j_multipredictwarn##|_new:predictions at fixed values of the }{help j_multipredictwarn##|_new:covariates.}{p_end}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiffonpanel=(-.6531436 1.756563))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}    29,541
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~l}{space 4}{txt:=} {space 2}-.6531436}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~l}{space 4}{txt:=} {space 3}1.756563}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     2.19{col 38}{space 2}   0.1387
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 75.87547{col 26}{space 2} 51.24631{col 37}{space 5}-24.56544{col 51}{space 3} 176.3164
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 9}{help j_multipredictwarn##|_new:Warning: Multiple }{help j_multipredictwarn##|_new:observations per subject are detected.  }{help j_multipredictwarn##|_new:Predictions that require averaging over }{help j_multipredictwarn##|_new:the dataset may not be appropriate.  }{help j_multipredictwarn##|_new:Use the {bf:at()} option to compute }{help j_multipredictwarn##|_new:predictions at fixed values of the }{help j_multipredictwarn##|_new:covariates.}{p_end}
{res}{txt}
{com}. 
. matrix modelC71bzloyal = r(table)
{txt}
{com}. mat list modelC71bzloyal
{res}
{txt}modelC71bzloyal[9,1]
             r2vs1.
               _at
     b {res}  75.875474
{txt}    se {res}  51.246307
{txt}     z {res}  1.4806038
{txt}pvalue {res}  .13871219
{txt}    ll {res} -24.565441
{txt}    ul {res}  176.31639
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. 
. 
. 
. 
. 
. 
. **** MODEL C8: WEIBULL MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg  c.zloyalmedian##i.soubinaryagency2onpanel  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign abssenpartydiffmean absfilipresdistancey okcrossover avgpresapp  unemployment i. okstartadyr  i.sbagency reagan bush41 clinton bush43, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
                 {txt}id:  {res}obsident
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-3798.4217
{txt}Iteration 1:   log pseudolikelihood = {res}-2630.2658
{txt}Iteration 2:   log pseudolikelihood = {res}-2017.4921
{txt}Iteration 3:   log pseudolikelihood = {res} -1936.816
{txt}Iteration 4:   log pseudolikelihood = {res}-1935.8198
{txt}Iteration 5:   log pseudolikelihood = {res}-1935.8196

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1935.8196}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1603.6748}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1587.3405}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1586.9245}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1586.9237}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1586.9237}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}    29,541
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}       29541
{col 49}{help j_robustsingular##|_new:Wald chi2(22)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-1586.9237             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 104:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 39}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 40}{c |}{col 52}    Robust
{col 1}                                    _t{col 40}{c |} Haz. Ratio{col 52}   Std. Err.{col 64}      z{col 72}   P>|z|{col 80}     [95% Con{col 93}f. Interval]
{hline 39}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}zloyalmedian {c |}{col 40}{res}{space 2} 1.386175{col 52}{space 2} .1964534{col 63}{space 1}    2.30{col 72}{space 3}0.021{col 80}{space 4} 1.049984{col 93}{space 3} 1.830009
{txt}{space 13}1.soubinaryagency2onpanel {c |}{col 40}{res}{space 2} 1.257274{col 52}{space 2} .1578768{col 63}{space 1}    1.82{col 72}{space 3}0.068{col 80}{space 4} .9829784{col 93}{space 3} 1.608111
{txt}{space 38} {c |}
soubinaryagency2onpanel#c.zloyalmedian {c |}
{space 36}1  {c |}{col 40}{res}{space 2} .7450618{col 52}{space 2} .0935979{col 63}{space 1}   -2.34{col 72}{space 3}0.019{col 80}{space 4} .5824528{col 93}{space 3} .9530681
{txt}{space 38} {c |}
{space 25}zpecompmedian {c |}{col 40}{res}{space 2} .8924063{col 52}{space 2} .0894736{col 63}{space 1}   -1.14{col 72}{space 3}0.256{col 80}{space 4} .7331963{col 93}{space 3} 1.086188
{txt}{space 25}zmecompmedian {c |}{col 40}{res}{space 2} .6889614{col 52}{space 2} .0421443{col 63}{space 1}   -6.09{col 72}{space 3}0.000{col 80}{space 4} .6111196{col 93}{space 3} .7767184
{txt}{space 29}toplevel2 {c |}{col 40}{res}{space 2} .8273774{col 52}{space 2} .1311885{col 63}{space 1}   -1.20{col 72}{space 3}0.232{col 80}{space 4} .6063699{col 93}{space 3} 1.128937
{txt}{space 18}presagencyideolalign {c |}{col 40}{res}{space 2}  .325086{col 52}{space 2} .0718771{col 63}{space 1}   -5.08{col 72}{space 3}0.000{col 80}{space 4} .2107639{col 93}{space 3} .5014185
{txt}{space 16}presagencyideolopposed {c |}{col 40}{res}{space 2} .3274723{col 52}{space 2} .0659246{col 63}{space 1}   -5.55{col 72}{space 3}0.000{col 80}{space 4}  .220707{col 93}{space 3} .4858845
{txt}{space 23}subagencydesign {c |}{col 40}{res}{space 2} 1.963555{col 52}{space 2} .3436394{col 63}{space 1}    3.86{col 72}{space 3}0.000{col 80}{space 4} 1.393399{col 93}{space 3} 2.767011
{txt}{space 16}standaloneagencydesign {c |}{col 40}{res}{space 2} 2.592704{col 52}{space 2} .7344731{col 63}{space 1}    3.36{col 72}{space 3}0.001{col 80}{space 4}  1.48806{col 93}{space 3} 4.517365
{txt}{space 19}abssenpartydiffmean {c |}{col 40}{res}{space 2} 839.9176{col 52}{space 2} 4991.747{col 63}{space 1}    1.13{col 72}{space 3}0.257{col 80}{space 4} .0073355{col 93}{space 3} 9.62e+07
{txt}{space 18}absfilipresdistancey {c |}{col 40}{res}{space 2} .0230327{col 52}{space 2} .0187322{col 63}{space 1}   -4.64{col 72}{space 3}0.000{col 80}{space 4} .0046782{col 93}{space 3} .1134004
{txt}{space 27}okcrossover {c |}{col 40}{res}{space 2} 1.081487{col 52}{space 2} .2152491{col 63}{space 1}    0.39{col 72}{space 3}0.694{col 80}{space 4} .7321605{col 93}{space 3} 1.597485
{txt}{space 28}avgpresapp {c |}{col 40}{res}{space 2} 1.009872{col 52}{space 2} .0044189{col 63}{space 1}    2.25{col 72}{space 3}0.025{col 80}{space 4} 1.001248{col 93}{space 3}  1.01857
{txt}{space 26}unemployment {c |}{col 40}{res}{space 2}   .89732{col 52}{space 2} .0350575{col 63}{space 1}   -2.77{col 72}{space 3}0.006{col 80}{space 4} .8311733{col 93}{space 3} .9687307
{txt}{space 38} {c |}
{space 27}okstartadyr {c |}
{space 36}2  {c |}{col 40}{res}{space 2} 3.570299{col 52}{space 2} .4306907{col 63}{space 1}   10.55{col 72}{space 3}0.000{col 80}{space 4} 2.818531{col 93}{space 3} 4.522581
{txt}{space 36}3  {c |}{col 40}{res}{space 2} 3.853526{col 52}{space 2} .4749378{col 63}{space 1}   10.95{col 72}{space 3}0.000{col 80}{space 4} 3.026563{col 93}{space 3} 4.906444
{txt}{space 36}4  {c |}{col 40}{res}{space 2} 3.999544{col 52}{space 2} .5065726{col 63}{space 1}   10.94{col 72}{space 3}0.000{col 80}{space 4} 3.120321{col 93}{space 3} 5.126507
{txt}{space 36}5  {c |}{col 40}{res}{space 2} 2.499359{col 52}{space 2} .4936081{col 63}{space 1}    4.64{col 72}{space 3}0.000{col 80}{space 4} 1.697155{col 93}{space 3} 3.680745
{txt}{space 36}6  {c |}{col 40}{res}{space 2} 2.470291{col 52}{space 2} .5079923{col 63}{space 1}    4.40{col 72}{space 3}0.000{col 80}{space 4} 1.650845{col 93}{space 3} 3.696491
{txt}{space 36}7  {c |}{col 40}{res}{space 2}  3.10773{col 52}{space 2} .6182089{col 63}{space 1}    5.70{col 72}{space 3}0.000{col 80}{space 4} 2.104345{col 93}{space 3} 4.589545
{txt}{space 36}8  {c |}{col 40}{res}{space 2} 2.850663{col 52}{space 2} .5314574{col 63}{space 1}    5.62{col 72}{space 3}0.000{col 80}{space 4} 1.978126{col 93}{space 3}  4.10807
{txt}{space 38} {c |}
{space 30}sbagency {c |}
{space 36}2  {c |}{col 40}{res}{space 2} 2.510839{col 52}{space 2} .5035818{col 63}{space 1}    4.59{col 72}{space 3}0.000{col 80}{space 4} 1.694725{col 93}{space 3} 3.719962
{txt}{space 36}3  {c |}{col 40}{res}{space 2}  1.86785{col 52}{space 2} .3832368{col 63}{space 1}    3.05{col 72}{space 3}0.002{col 80}{space 4} 1.249385{col 93}{space 3} 2.792464
{txt}{space 36}4  {c |}{col 40}{res}{space 2} 1.460223{col 52}{space 2} .2423502{col 63}{space 1}    2.28{col 72}{space 3}0.023{col 80}{space 4} 1.054744{col 93}{space 3} 2.021582
{txt}{space 36}5  {c |}{col 40}{res}{space 2} 3.337025{col 52}{space 2} .6167241{col 63}{space 1}    6.52{col 72}{space 3}0.000{col 80}{space 4} 2.322987{col 93}{space 3} 4.793715
{txt}{space 36}6  {c |}{col 40}{res}{space 2} 3.515554{col 52}{space 2} .7071009{col 63}{space 1}    6.25{col 72}{space 3}0.000{col 80}{space 4} 2.370213{col 93}{space 3}  5.21435
{txt}{space 36}7  {c |}{col 40}{res}{space 2} 2.266986{col 52}{space 2} .4351268{col 63}{space 1}    4.26{col 72}{space 3}0.000{col 80}{space 4} 1.556211{col 93}{space 3} 3.302395
{txt}{space 36}8  {c |}{col 40}{res}{space 2} 4.266829{col 52}{space 2} .7890017{col 63}{space 1}    7.85{col 72}{space 3}0.000{col 80}{space 4} 2.969649{col 93}{space 3} 6.130633
{txt}{space 36}9  {c |}{col 40}{res}{space 2} 3.541156{col 52}{space 2} .6436411{col 63}{space 1}    6.96{col 72}{space 3}0.000{col 80}{space 4} 2.479879{col 93}{space 3} 5.056612
{txt}{space 35}11  {c |}{col 40}{res}{space 2} 3.207883{col 52}{space 2}  .798719{col 63}{space 1}    4.68{col 72}{space 3}0.000{col 80}{space 4}  1.96916{col 93}{space 3} 5.225839
{txt}{space 35}12  {c |}{col 40}{res}{space 2} 3.169697{col 52}{space 2}  .644473{col 63}{space 1}    5.67{col 72}{space 3}0.000{col 80}{space 4} 2.127888{col 93}{space 3} 4.721574
{txt}{space 35}13  {c |}{col 40}{res}{space 2} 3.537209{col 52}{space 2} .7956796{col 63}{space 1}    5.62{col 72}{space 3}0.000{col 80}{space 4} 2.276076{col 93}{space 3} 5.497115
{txt}{space 35}14  {c |}{col 40}{res}{space 2} 2.687993{col 52}{space 2} .4969762{col 63}{space 1}    5.35{col 72}{space 3}0.000{col 80}{space 4} 1.870905{col 93}{space 3} 3.861932
{txt}{space 35}15  {c |}{col 40}{res}{space 2} 2.363756{col 52}{space 2} .2990888{col 63}{space 1}    6.80{col 72}{space 3}0.000{col 80}{space 4} 1.844587{col 93}{space 3} 3.029048
{txt}{space 35}16  {c |}{col 40}{res}{space 2} 1.014826{col 52}{space 2} .2225805{col 63}{space 1}    0.07{col 72}{space 3}0.947{col 80}{space 4}  .660235{col 93}{space 3} 1.559856
{txt}{space 35}17  {c |}{col 40}{res}{space 2} 1.228358{col 52}{space 2} .1234575{col 63}{space 1}    2.05{col 72}{space 3}0.041{col 80}{space 4} 1.008728{col 93}{space 3} 1.495808
{txt}{space 35}18  {c |}{col 40}{res}{space 2} 2.393504{col 52}{space 2} .3931975{col 63}{space 1}    5.31{col 72}{space 3}0.000{col 80}{space 4} 1.734608{col 93}{space 3} 3.302683
{txt}{space 35}19  {c |}{col 40}{res}{space 2} .8484025{col 52}{space 2} .1752785{col 63}{space 1}   -0.80{col 72}{space 3}0.426{col 80}{space 4} .5659071{col 93}{space 3} 1.271917
{txt}{space 35}20  {c |}{col 40}{res}{space 2} .0793157{col 52}{space 2} .0251478{col 63}{space 1}   -7.99{col 72}{space 3}0.000{col 80}{space 4} .0426066{col 93}{space 3} .1476525
{txt}{space 35}21  {c |}{col 40}{res}{space 2} 1.430486{col 52}{space 2} .3693659{col 63}{space 1}    1.39{col 72}{space 3}0.166{col 80}{space 4} .8623722{col 93}{space 3} 2.372862
{txt}{space 35}22  {c |}{col 40}{res}{space 2} .4365482{col 52}{space 2} .1232854{col 63}{space 1}   -2.93{col 72}{space 3}0.003{col 80}{space 4} .2509832{col 93}{space 3} .7593112
{txt}{space 35}23  {c |}{col 40}{res}{space 2}  .536519{col 52}{space 2} .1394405{col 63}{space 1}   -2.40{col 72}{space 3}0.017{col 80}{space 4} .3223733{col 93}{space 3} .8929173
{txt}{space 35}24  {c |}{col 40}{res}{space 2} .0682417{col 52}{space 2} .0221128{col 63}{space 1}   -8.29{col 72}{space 3}0.000{col 80}{space 4} .0361602{col 93}{space 3} .1287863
{txt}{space 35}25  {c |}{col 40}{res}{space 2}  .865565{col 52}{space 2} .2972918{col 63}{space 1}   -0.42{col 72}{space 3}0.674{col 80}{space 4} .4415107{col 93}{space 3} 1.696907
{txt}{space 35}26  {c |}{col 40}{res}{space 2} .7361732{col 52}{space 2} .1544007{col 63}{space 1}   -1.46{col 72}{space 3}0.144{col 80}{space 4} .4880383{col 93}{space 3} 1.110468
{txt}{space 35}27  {c |}{col 40}{res}{space 2}        1{col 52}{txt}  (omitted)
{space 35}28  {c |}{col 40}{res}{space 2} 1.896106{col 52}{space 2} .2618517{col 63}{space 1}    4.63{col 72}{space 3}0.000{col 80}{space 4} 1.446478{col 93}{space 3} 2.485497
{txt}{space 35}29  {c |}{col 40}{res}{space 2} 3.735223{col 52}{space 2}   1.0706{col 63}{space 1}    4.60{col 72}{space 3}0.000{col 80}{space 4} 2.129819{col 93}{space 3} 6.550739
{txt}{space 35}30  {c |}{col 40}{res}{space 2} 3.761944{col 52}{space 2} .9965145{col 63}{space 1}    5.00{col 72}{space 3}0.000{col 80}{space 4} 2.238384{col 93}{space 3} 6.322518
{txt}{space 35}50  {c |}{col 40}{res}{space 2} 1.545808{col 52}{space 2} .3480252{col 63}{space 1}    1.93{col 72}{space 3}0.053{col 80}{space 4} .9942945{col 93}{space 3} 2.403235
{txt}{space 35}51  {c |}{col 40}{res}{space 2}  2.39559{col 52}{space 2} .5091608{col 63}{space 1}    4.11{col 72}{space 3}0.000{col 80}{space 4} 1.579419{col 93}{space 3}  3.63352
{txt}{space 35}52  {c |}{col 40}{res}{space 2} .3523508{col 52}{space 2} .0734358{col 63}{space 1}   -5.01{col 72}{space 3}0.000{col 80}{space 4} .2341912{col 93}{space 3} .5301269
{txt}{space 35}53  {c |}{col 40}{res}{space 2} .9449932{col 52}{space 2} .0524261{col 63}{space 1}   -1.02{col 72}{space 3}0.308{col 80}{space 4} .8476292{col 93}{space 3} 1.053541
{txt}{space 35}54  {c |}{col 40}{res}{space 2}  .291427{col 52}{space 2} .0717508{col 63}{space 1}   -5.01{col 72}{space 3}0.000{col 80}{space 4} .1798704{col 93}{space 3} .4721716
{txt}{space 35}55  {c |}{col 40}{res}{space 2} 1.524784{col 52}{space 2} .4746017{col 63}{space 1}    1.36{col 72}{space 3}0.175{col 80}{space 4} .8284468{col 93}{space 3} 2.806415
{txt}{space 35}56  {c |}{col 40}{res}{space 2} 2.249438{col 52}{space 2} .6055427{col 63}{space 1}    3.01{col 72}{space 3}0.003{col 80}{space 4} 1.327189{col 93}{space 3} 3.812547
{txt}{space 35}57  {c |}{col 40}{res}{space 2}        1{col 52}{txt}  (omitted)
{space 35}58  {c |}{col 40}{res}{space 2} .4311027{col 52}{space 2} .1262621{col 63}{space 1}   -2.87{col 72}{space 3}0.004{col 80}{space 4} .2428171{col 93}{space 3} .7653888
{txt}{space 35}59  {c |}{col 40}{res}{space 2}  .262983{col 52}{space 2} .1193465{col 63}{space 1}   -2.94{col 72}{space 3}0.003{col 80}{space 4} .1080531{col 93}{space 3} .6400566
{txt}{space 35}60  {c |}{col 40}{res}{space 2} .9975539{col 52}{space 2} .2567479{col 63}{space 1}   -0.01{col 72}{space 3}0.992{col 80}{space 4} .6023601{col 93}{space 3} 1.652025
{txt}{space 35}61  {c |}{col 40}{res}{space 2}        1{col 52}{txt}  (omitted)
{space 38} {c |}
{space 32}reagan {c |}{col 40}{res}{space 2}  7.48716{col 52}{space 2} 2.009723{col 63}{space 1}    7.50{col 72}{space 3}0.000{col 80}{space 4} 4.424206{col 93}{space 3} 12.67065
{txt}{space 32}bush41 {c |}{col 40}{res}{space 2} 2.131847{col 52}{space 2} .8313305{col 63}{space 1}    1.94{col 72}{space 3}0.052{col 80}{space 4} .9927116{col 93}{space 3} 4.578137
{txt}{space 31}clinton {c |}{col 40}{res}{space 2} 2.211585{col 52}{space 2} .9243499{col 63}{space 1}    1.90{col 72}{space 3}0.058{col 80}{space 4} .9748483{col 93}{space 3} 5.017302
{txt}{space 32}bush43 {c |}{col 40}{res}{space 2} 5.170926{col 52}{space 2} 2.361209{col 63}{space 1}    3.60{col 72}{space 3}0.000{col 80}{space 4} 2.112918{col 93}{space 3} 12.65476
{txt}{space 33}_cons {c |}{col 40}{res}{space 2} .0247553{col 52}{space 2}  .079026{col 63}{space 1}   -1.16{col 72}{space 3}0.247{col 80}{space 4} .0000475{col 93}{space 3} 12.91062
{txt}{hline 39}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 33}/ln_p {c |}{col 40}{res}{space 2}-.3609085{col 52}{space 2}   .05966{col 63}{space 1}   -6.05{col 72}{space 3}0.000{col 80}{space 4}  -.47784{col 93}{space 3} -.243977
{txt}{hline 39}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                     p {c |}{col 40}{res}{space 2} .6970428{col 52}{space 2} .0415856{col 80}{space 4} .6201214{col 93}{space 3} .7835057
{txt}                                   1/p {c |}{col 40}{res}{space 2} 1.434632{col 52}{space 2} .0855902{col 80}{space 4} 1.276315{col 93}{space 3} 1.612587
{txt}{hline 39}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}    29,541{col 28} -1935.82{col 39}-1586.924{col 50}    24{col 58} 3221.847{col 69} 3420.892
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. 
. 
. *** COMPUTE Figure C1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}STANDALONE − NON-STANDALONE Difference{c )-} {c -(}{c -(}2 [M2 & M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE:  IQ = 1.3688693 [0.9781515 - (-0.3907178)]
. 
. lincomest 1.soubinaryagency2onpanel#c.zloyalmedian*1.3688693, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2onpanel#c.zloyalmedian*1.3688693

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .6684178{col 26}{space 2} .1149433{col 37}{space 1}   -2.34{col 46}{space 3}0.019{col 54}{space 4} .4771694{col 67}{space 3}  .936318
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelC8zloyal = r(table)
{txt}
{com}. mat list modelC8zloyal
{res}
{txt}modelC8zloyal[9,1]
               (1)
     b {res}  .66841777
{txt}    se {res}   .1149433
{txt}     z {res} -2.3426044
{txt}pvalue {res}  .01914968
{txt}    ll {res}  .47716941
{txt}    ul {res}  .93631803
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. **** COMPUTE Figure C2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. **  NOTE: IQ = 1.3688693 [0.9781515 - (-0.3907178)] **
. 
. drop loyalppdiffonpanel
{txt}
{com}. generate loyalppdiffonpanel = soubinaryagency2onpanel*zloyalmedian
{txt}
{com}. 
. ** Re-Estimate Model C8  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2onpanel loyalppdiffonpanel  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign abssenpartydiffmean absfilipresdistancey okcrossover avgpresapp  unemployment i. okstartadyr  i.sbagency reagan bush41 clinton bush43, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
                 {txt}id:  {res}obsident
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-3798.4217
{txt}Iteration 1:   log pseudolikelihood = {res}-2630.2658
{txt}Iteration 2:   log pseudolikelihood = {res}-2017.4921
{txt}Iteration 3:   log pseudolikelihood = {res} -1936.816
{txt}Iteration 4:   log pseudolikelihood = {res}-1935.8198
{txt}Iteration 5:   log pseudolikelihood = {res}-1935.8196

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1935.8196}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1603.6748}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1587.3405}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1586.9245}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1586.9237}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1586.9237}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}    29,541
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}       29541
{col 49}{help j_robustsingular##|_new:Wald chi2(22)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-1586.9237             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 89:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                     _t{col 25}{c |} Haz. Ratio{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}zloyalmedian {c |}{col 25}{res}{space 2} 1.386175{col 37}{space 2} .1964534{col 48}{space 1}    2.30{col 57}{space 3}0.021{col 65}{space 4} 1.049984{col 78}{space 3} 1.830009
{txt}soubinaryagency2onpanel {c |}{col 25}{res}{space 2} 1.257274{col 37}{space 2} .1578768{col 48}{space 1}    1.82{col 57}{space 3}0.068{col 65}{space 4} .9829784{col 78}{space 3} 1.608111
{txt}{space 5}loyalppdiffonpanel {c |}{col 25}{res}{space 2} .7450618{col 37}{space 2} .0935979{col 48}{space 1}   -2.34{col 57}{space 3}0.019{col 65}{space 4} .5824528{col 78}{space 3} .9530681
{txt}{space 10}zpecompmedian {c |}{col 25}{res}{space 2} .8924063{col 37}{space 2} .0894736{col 48}{space 1}   -1.14{col 57}{space 3}0.256{col 65}{space 4} .7331963{col 78}{space 3} 1.086188
{txt}{space 10}zmecompmedian {c |}{col 25}{res}{space 2} .6889614{col 37}{space 2} .0421443{col 48}{space 1}   -6.09{col 57}{space 3}0.000{col 65}{space 4} .6111196{col 78}{space 3} .7767184
{txt}{space 14}toplevel2 {c |}{col 25}{res}{space 2} .8273774{col 37}{space 2} .1311885{col 48}{space 1}   -1.20{col 57}{space 3}0.232{col 65}{space 4} .6063699{col 78}{space 3} 1.128937
{txt}{space 3}presagencyideolalign {c |}{col 25}{res}{space 2}  .325086{col 37}{space 2} .0718771{col 48}{space 1}   -5.08{col 57}{space 3}0.000{col 65}{space 4} .2107639{col 78}{space 3} .5014185
{txt}{space 1}presagencyideolopposed {c |}{col 25}{res}{space 2} .3274723{col 37}{space 2} .0659246{col 48}{space 1}   -5.55{col 57}{space 3}0.000{col 65}{space 4}  .220707{col 78}{space 3} .4858845
{txt}{space 8}subagencydesign {c |}{col 25}{res}{space 2} 1.963555{col 37}{space 2} .3436394{col 48}{space 1}    3.86{col 57}{space 3}0.000{col 65}{space 4} 1.393399{col 78}{space 3} 2.767011
{txt}{space 1}standaloneagencydesign {c |}{col 25}{res}{space 2} 2.592704{col 37}{space 2} .7344731{col 48}{space 1}    3.36{col 57}{space 3}0.001{col 65}{space 4}  1.48806{col 78}{space 3} 4.517365
{txt}{space 4}abssenpartydiffmean {c |}{col 25}{res}{space 2} 839.9176{col 37}{space 2} 4991.747{col 48}{space 1}    1.13{col 57}{space 3}0.257{col 65}{space 4} .0073355{col 78}{space 3} 9.62e+07
{txt}{space 3}absfilipresdistancey {c |}{col 25}{res}{space 2} .0230327{col 37}{space 2} .0187322{col 48}{space 1}   -4.64{col 57}{space 3}0.000{col 65}{space 4} .0046782{col 78}{space 3} .1134004
{txt}{space 12}okcrossover {c |}{col 25}{res}{space 2} 1.081487{col 37}{space 2} .2152491{col 48}{space 1}    0.39{col 57}{space 3}0.694{col 65}{space 4} .7321605{col 78}{space 3} 1.597485
{txt}{space 13}avgpresapp {c |}{col 25}{res}{space 2} 1.009872{col 37}{space 2} .0044189{col 48}{space 1}    2.25{col 57}{space 3}0.025{col 65}{space 4} 1.001248{col 78}{space 3}  1.01857
{txt}{space 11}unemployment {c |}{col 25}{res}{space 2}   .89732{col 37}{space 2} .0350575{col 48}{space 1}   -2.77{col 57}{space 3}0.006{col 65}{space 4} .8311733{col 78}{space 3} .9687307
{txt}{space 23} {c |}
{space 12}okstartadyr {c |}
{space 21}2  {c |}{col 25}{res}{space 2} 3.570299{col 37}{space 2} .4306907{col 48}{space 1}   10.55{col 57}{space 3}0.000{col 65}{space 4} 2.818531{col 78}{space 3} 4.522581
{txt}{space 21}3  {c |}{col 25}{res}{space 2} 3.853526{col 37}{space 2} .4749378{col 48}{space 1}   10.95{col 57}{space 3}0.000{col 65}{space 4} 3.026563{col 78}{space 3} 4.906444
{txt}{space 21}4  {c |}{col 25}{res}{space 2} 3.999544{col 37}{space 2} .5065726{col 48}{space 1}   10.94{col 57}{space 3}0.000{col 65}{space 4} 3.120321{col 78}{space 3} 5.126507
{txt}{space 21}5  {c |}{col 25}{res}{space 2} 2.499359{col 37}{space 2} .4936081{col 48}{space 1}    4.64{col 57}{space 3}0.000{col 65}{space 4} 1.697155{col 78}{space 3} 3.680745
{txt}{space 21}6  {c |}{col 25}{res}{space 2} 2.470291{col 37}{space 2} .5079923{col 48}{space 1}    4.40{col 57}{space 3}0.000{col 65}{space 4} 1.650845{col 78}{space 3} 3.696491
{txt}{space 21}7  {c |}{col 25}{res}{space 2}  3.10773{col 37}{space 2} .6182089{col 48}{space 1}    5.70{col 57}{space 3}0.000{col 65}{space 4} 2.104345{col 78}{space 3} 4.589545
{txt}{space 21}8  {c |}{col 25}{res}{space 2} 2.850663{col 37}{space 2} .5314574{col 48}{space 1}    5.62{col 57}{space 3}0.000{col 65}{space 4} 1.978126{col 78}{space 3}  4.10807
{txt}{space 23} {c |}
{space 15}sbagency {c |}
{space 21}2  {c |}{col 25}{res}{space 2} 2.510839{col 37}{space 2} .5035818{col 48}{space 1}    4.59{col 57}{space 3}0.000{col 65}{space 4} 1.694725{col 78}{space 3} 3.719962
{txt}{space 21}3  {c |}{col 25}{res}{space 2}  1.86785{col 37}{space 2} .3832368{col 48}{space 1}    3.05{col 57}{space 3}0.002{col 65}{space 4} 1.249385{col 78}{space 3} 2.792464
{txt}{space 21}4  {c |}{col 25}{res}{space 2} 1.460223{col 37}{space 2} .2423502{col 48}{space 1}    2.28{col 57}{space 3}0.023{col 65}{space 4} 1.054744{col 78}{space 3} 2.021582
{txt}{space 21}5  {c |}{col 25}{res}{space 2} 3.337025{col 37}{space 2} .6167241{col 48}{space 1}    6.52{col 57}{space 3}0.000{col 65}{space 4} 2.322987{col 78}{space 3} 4.793715
{txt}{space 21}6  {c |}{col 25}{res}{space 2} 3.515554{col 37}{space 2} .7071009{col 48}{space 1}    6.25{col 57}{space 3}0.000{col 65}{space 4} 2.370213{col 78}{space 3}  5.21435
{txt}{space 21}7  {c |}{col 25}{res}{space 2} 2.266986{col 37}{space 2} .4351268{col 48}{space 1}    4.26{col 57}{space 3}0.000{col 65}{space 4} 1.556211{col 78}{space 3} 3.302395
{txt}{space 21}8  {c |}{col 25}{res}{space 2} 4.266829{col 37}{space 2} .7890017{col 48}{space 1}    7.85{col 57}{space 3}0.000{col 65}{space 4} 2.969649{col 78}{space 3} 6.130633
{txt}{space 21}9  {c |}{col 25}{res}{space 2} 3.541156{col 37}{space 2} .6436411{col 48}{space 1}    6.96{col 57}{space 3}0.000{col 65}{space 4} 2.479879{col 78}{space 3} 5.056612
{txt}{space 20}11  {c |}{col 25}{res}{space 2} 3.207883{col 37}{space 2}  .798719{col 48}{space 1}    4.68{col 57}{space 3}0.000{col 65}{space 4}  1.96916{col 78}{space 3} 5.225839
{txt}{space 20}12  {c |}{col 25}{res}{space 2} 3.169697{col 37}{space 2}  .644473{col 48}{space 1}    5.67{col 57}{space 3}0.000{col 65}{space 4} 2.127888{col 78}{space 3} 4.721574
{txt}{space 20}13  {c |}{col 25}{res}{space 2} 3.537209{col 37}{space 2} .7956796{col 48}{space 1}    5.62{col 57}{space 3}0.000{col 65}{space 4} 2.276076{col 78}{space 3} 5.497115
{txt}{space 20}14  {c |}{col 25}{res}{space 2} 2.687993{col 37}{space 2} .4969762{col 48}{space 1}    5.35{col 57}{space 3}0.000{col 65}{space 4} 1.870905{col 78}{space 3} 3.861932
{txt}{space 20}15  {c |}{col 25}{res}{space 2} 2.363756{col 37}{space 2} .2990888{col 48}{space 1}    6.80{col 57}{space 3}0.000{col 65}{space 4} 1.844587{col 78}{space 3} 3.029048
{txt}{space 20}16  {c |}{col 25}{res}{space 2} 1.014826{col 37}{space 2} .2225805{col 48}{space 1}    0.07{col 57}{space 3}0.947{col 65}{space 4}  .660235{col 78}{space 3} 1.559856
{txt}{space 20}17  {c |}{col 25}{res}{space 2} 1.228358{col 37}{space 2} .1234575{col 48}{space 1}    2.05{col 57}{space 3}0.041{col 65}{space 4} 1.008728{col 78}{space 3} 1.495808
{txt}{space 20}18  {c |}{col 25}{res}{space 2} 2.393504{col 37}{space 2} .3931975{col 48}{space 1}    5.31{col 57}{space 3}0.000{col 65}{space 4} 1.734608{col 78}{space 3} 3.302683
{txt}{space 20}19  {c |}{col 25}{res}{space 2} .8484025{col 37}{space 2} .1752785{col 48}{space 1}   -0.80{col 57}{space 3}0.426{col 65}{space 4} .5659071{col 78}{space 3} 1.271917
{txt}{space 20}20  {c |}{col 25}{res}{space 2} .0793157{col 37}{space 2} .0251478{col 48}{space 1}   -7.99{col 57}{space 3}0.000{col 65}{space 4} .0426066{col 78}{space 3} .1476525
{txt}{space 20}21  {c |}{col 25}{res}{space 2} 1.430486{col 37}{space 2} .3693659{col 48}{space 1}    1.39{col 57}{space 3}0.166{col 65}{space 4} .8623722{col 78}{space 3} 2.372862
{txt}{space 20}22  {c |}{col 25}{res}{space 2} .4365482{col 37}{space 2} .1232854{col 48}{space 1}   -2.93{col 57}{space 3}0.003{col 65}{space 4} .2509832{col 78}{space 3} .7593112
{txt}{space 20}23  {c |}{col 25}{res}{space 2}  .536519{col 37}{space 2} .1394405{col 48}{space 1}   -2.40{col 57}{space 3}0.017{col 65}{space 4} .3223733{col 78}{space 3} .8929173
{txt}{space 20}24  {c |}{col 25}{res}{space 2} .0682417{col 37}{space 2} .0221128{col 48}{space 1}   -8.29{col 57}{space 3}0.000{col 65}{space 4} .0361602{col 78}{space 3} .1287863
{txt}{space 20}25  {c |}{col 25}{res}{space 2}  .865565{col 37}{space 2} .2972918{col 48}{space 1}   -0.42{col 57}{space 3}0.674{col 65}{space 4} .4415107{col 78}{space 3} 1.696907
{txt}{space 20}26  {c |}{col 25}{res}{space 2} .7361732{col 37}{space 2} .1544007{col 48}{space 1}   -1.46{col 57}{space 3}0.144{col 65}{space 4} .4880383{col 78}{space 3} 1.110468
{txt}{space 20}27  {c |}{col 25}{res}{space 2}        1{col 37}{txt}  (omitted)
{space 20}28  {c |}{col 25}{res}{space 2} 1.896106{col 37}{space 2} .2618517{col 48}{space 1}    4.63{col 57}{space 3}0.000{col 65}{space 4} 1.446478{col 78}{space 3} 2.485497
{txt}{space 20}29  {c |}{col 25}{res}{space 2} 3.735223{col 37}{space 2}   1.0706{col 48}{space 1}    4.60{col 57}{space 3}0.000{col 65}{space 4} 2.129819{col 78}{space 3} 6.550739
{txt}{space 20}30  {c |}{col 25}{res}{space 2} 3.761944{col 37}{space 2} .9965145{col 48}{space 1}    5.00{col 57}{space 3}0.000{col 65}{space 4} 2.238384{col 78}{space 3} 6.322518
{txt}{space 20}50  {c |}{col 25}{res}{space 2} 1.545808{col 37}{space 2} .3480252{col 48}{space 1}    1.93{col 57}{space 3}0.053{col 65}{space 4} .9942945{col 78}{space 3} 2.403235
{txt}{space 20}51  {c |}{col 25}{res}{space 2}  2.39559{col 37}{space 2} .5091608{col 48}{space 1}    4.11{col 57}{space 3}0.000{col 65}{space 4} 1.579419{col 78}{space 3}  3.63352
{txt}{space 20}52  {c |}{col 25}{res}{space 2} .3523508{col 37}{space 2} .0734358{col 48}{space 1}   -5.01{col 57}{space 3}0.000{col 65}{space 4} .2341912{col 78}{space 3} .5301269
{txt}{space 20}53  {c |}{col 25}{res}{space 2} .9449932{col 37}{space 2} .0524261{col 48}{space 1}   -1.02{col 57}{space 3}0.308{col 65}{space 4} .8476292{col 78}{space 3} 1.053541
{txt}{space 20}54  {c |}{col 25}{res}{space 2}  .291427{col 37}{space 2} .0717508{col 48}{space 1}   -5.01{col 57}{space 3}0.000{col 65}{space 4} .1798704{col 78}{space 3} .4721716
{txt}{space 20}55  {c |}{col 25}{res}{space 2} 1.524784{col 37}{space 2} .4746017{col 48}{space 1}    1.36{col 57}{space 3}0.175{col 65}{space 4} .8284468{col 78}{space 3} 2.806415
{txt}{space 20}56  {c |}{col 25}{res}{space 2} 2.249438{col 37}{space 2} .6055427{col 48}{space 1}    3.01{col 57}{space 3}0.003{col 65}{space 4} 1.327189{col 78}{space 3} 3.812547
{txt}{space 20}57  {c |}{col 25}{res}{space 2}        1{col 37}{txt}  (omitted)
{space 20}58  {c |}{col 25}{res}{space 2} .4311027{col 37}{space 2} .1262621{col 48}{space 1}   -2.87{col 57}{space 3}0.004{col 65}{space 4} .2428171{col 78}{space 3} .7653888
{txt}{space 20}59  {c |}{col 25}{res}{space 2}  .262983{col 37}{space 2} .1193465{col 48}{space 1}   -2.94{col 57}{space 3}0.003{col 65}{space 4} .1080531{col 78}{space 3} .6400566
{txt}{space 20}60  {c |}{col 25}{res}{space 2} .9975539{col 37}{space 2} .2567479{col 48}{space 1}   -0.01{col 57}{space 3}0.992{col 65}{space 4} .6023601{col 78}{space 3} 1.652025
{txt}{space 20}61  {c |}{col 25}{res}{space 2}        1{col 37}{txt}  (omitted)
{space 23} {c |}
{space 17}reagan {c |}{col 25}{res}{space 2}  7.48716{col 37}{space 2} 2.009723{col 48}{space 1}    7.50{col 57}{space 3}0.000{col 65}{space 4} 4.424206{col 78}{space 3} 12.67065
{txt}{space 17}bush41 {c |}{col 25}{res}{space 2} 2.131847{col 37}{space 2} .8313305{col 48}{space 1}    1.94{col 57}{space 3}0.052{col 65}{space 4} .9927116{col 78}{space 3} 4.578137
{txt}{space 16}clinton {c |}{col 25}{res}{space 2} 2.211585{col 37}{space 2} .9243499{col 48}{space 1}    1.90{col 57}{space 3}0.058{col 65}{space 4} .9748483{col 78}{space 3} 5.017302
{txt}{space 17}bush43 {c |}{col 25}{res}{space 2} 5.170926{col 37}{space 2} 2.361209{col 48}{space 1}    3.60{col 57}{space 3}0.000{col 65}{space 4} 2.112918{col 78}{space 3} 12.65476
{txt}{space 18}_cons {c |}{col 25}{res}{space 2} .0247553{col 37}{space 2}  .079026{col 48}{space 1}   -1.16{col 57}{space 3}0.247{col 65}{space 4} .0000475{col 78}{space 3} 12.91062
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}/ln_p {c |}{col 25}{res}{space 2}-.3609085{col 37}{space 2}   .05966{col 48}{space 1}   -6.05{col 57}{space 3}0.000{col 65}{space 4}  -.47784{col 78}{space 3} -.243977
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                      p {c |}{col 25}{res}{space 2} .6970428{col 37}{space 2} .0415856{col 65}{space 4} .6201214{col 78}{space 3} .7835057
{txt}                    1/p {c |}{col 25}{res}{space 2} 1.434632{col 37}{space 2} .0855902{col 65}{space 4} 1.276315{col 78}{space 3} 1.612587
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimates store modelc81
{txt}
{com}. 
. *margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9710589))
. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. 
. margins, predict(median time) at(loyalppdiffonpanel=(-0.3960373 0.9710589))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}    29,541
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~l}{space 4}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~l}{space 4}{txt:=} {space 3}.9710589}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     3.03{col 38}{space 2}   0.0815
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 83.25916{col 26}{space 2} 47.79707{col 37}{space 5}-10.42138{col 51}{space 3} 176.9397
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 9}{help j_multipredictwarn##|_new:Warning: Multiple }{help j_multipredictwarn##|_new:observations per subject are detected.  }{help j_multipredictwarn##|_new:Predictions that require averaging over }{help j_multipredictwarn##|_new:the dataset may not be appropriate.  }{help j_multipredictwarn##|_new:Use the {bf:at()} option to compute }{help j_multipredictwarn##|_new:predictions at fixed values of the }{help j_multipredictwarn##|_new:covariates.}{p_end}
{res}{txt}
{com}. 
. matrix modelC81azloyal = r(table)
{txt}
{com}. mat list modelC81azloyal
{res}
{txt}modelC81azloyal[9,1]
             r2vs1.
               _at
     b {res}  83.259162
{txt}    se {res}  47.797075
{txt}     z {res}  1.7419301
{txt}pvalue {res}  .08152068
{txt}    ll {res} -10.421383
{txt}    ul {res}  176.93971
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. 
. 
. estimates restore modelc81
{txt}(results {stata estimates replay modelc81:modelc81} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiffonpanel=(-.6531436 1.756563))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}    29,541
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~l}{space 4}{txt:=} {space 2}-.6531436}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~l}{space 4}{txt:=} {space 3}1.756563}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 95.63844{col 26}{space 2} 12.99273{col 37}{space 1}    7.36{col 46}{space 3}0.000{col 54}{space 4} 70.17316{col 67}{space 3} 121.1037
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 264.5264{col 26}{space 2} 105.8618{col 37}{space 1}    2.50{col 46}{space 3}0.012{col 54}{space 4} 57.04105{col 67}{space 3} 472.0118
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 9}{help j_multipredictwarn##|_new:Warning: Multiple }{help j_multipredictwarn##|_new:observations per subject are detected.  }{help j_multipredictwarn##|_new:Predictions that require averaging over }{help j_multipredictwarn##|_new:the dataset may not be appropriate.  }{help j_multipredictwarn##|_new:Use the {bf:at()} option to compute }{help j_multipredictwarn##|_new:predictions at fixed values of the }{help j_multipredictwarn##|_new:covariates.}{p_end}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiffonpanel=(-.6531436 1.756563))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}    29,541
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdif~l}{space 4}{txt:=} {space 2}-.6531436}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdif~l}{space 4}{txt:=} {space 3}1.756563}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     2.39{col 38}{space 2}   0.1222
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2}  168.888{col 26}{space 2} 109.2695{col 37}{space 5}-45.27634{col 51}{space 3} 383.0523
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 9}{help j_multipredictwarn##|_new:Warning: Multiple }{help j_multipredictwarn##|_new:observations per subject are detected.  }{help j_multipredictwarn##|_new:Predictions that require averaging over }{help j_multipredictwarn##|_new:the dataset may not be appropriate.  }{help j_multipredictwarn##|_new:Use the {bf:at()} option to compute }{help j_multipredictwarn##|_new:predictions at fixed values of the }{help j_multipredictwarn##|_new:covariates.}{p_end}
{res}{txt}
{com}. 
. matrix modelC81bzloyal = r(table)
{txt}
{com}. mat list modelC81bzloyal
{res}
{txt}modelC81bzloyal[9,1]
             r2vs1.
               _at
     b {res}  168.88799
{txt}    se {res}  109.26952
{txt}     z {res}  1.5456093
{txt}pvalue {res}  .12219895
{txt}    ll {res} -45.276338
{txt}    ul {res}  383.05231
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. 
. 
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. *Figure 1
. 
. matrix A = J(4, 3, .)
{txt}
{com}. matrix coln A = Point ll95 ul95
{txt}
{com}. matrix rown A = 1 2 3 4
{txt}
{com}. 
. 
. matrix A[1,1] = modelC1zloyalnom[1,1]
{txt}
{com}. matrix A[1,2] = modelC1zloyalnom[5,1]
{txt}
{com}. matrix A[1,3] = modelC1zloyalnom[6,1]
{txt}
{com}. 
. 
. matrix A[2,1] = modelC2zloyalnom[1,1]
{txt}
{com}. matrix A[2,2] = modelC2zloyalnom[5,1]
{txt}
{com}. matrix A[2,3] = modelC2zloyalnom[6,1]
{txt}
{com}. 
. 
. matrix A[3,1] = modelC3zloyalnom[1,1]
{txt}
{com}. matrix A[3,2] = modelC3zloyalnom[5,1]
{txt}
{com}. matrix A[3,3] = modelC3zloyalnom[6,1]
{txt}
{com}. 
. 
. matrix A[4,1] = modelC4zloyalnom[1,1]
{txt}
{com}. matrix A[4,2] = modelC4zloyalnom[5,1]
{txt}
{com}. matrix A[4,3] = modelC4zloyalnom[6,1]
{txt}
{com}. 
. 
. 
. 
. 
. ********************
. 
. matrix B = J(4, 3, .)
{txt}
{com}. matrix coln B = Point ll95 ul95
{txt}
{com}. matrix rown B = 1 2 3 4
{txt}
{com}. 
. 
. matrix B[1,1] = modelC1zloyalonoff[1,1]
{txt}
{com}. matrix B[1,2] = modelC1zloyalonoff[5,1]
{txt}
{com}. matrix B[1,3] = modelC1zloyalonoff[6,1]
{txt}
{com}. 
. 
. 
. matrix B[2,1] = modelC2zloyalonoff[1,1]
{txt}
{com}. matrix B[2,2] = modelC2zloyalonoff[5,1]
{txt}
{com}. matrix B[2,3] = modelC2zloyalonoff[6,1]
{txt}
{com}. 
. 
. 
. matrix B[3,1] = modelC3zloyalonoff[1,1]
{txt}
{com}. matrix B[3,2] = modelC3zloyalonoff[5,1]
{txt}
{com}. matrix B[3,3] = modelC3zloyalonoff[6,1]
{txt}
{com}. 
. 
. 
. matrix B[4,1] = modelC4zloyalonoff[1,1]
{txt}
{com}. matrix B[4,2] = modelC4zloyalonoff[5,1]
{txt}
{com}. matrix B[4,3] = modelC4zloyalonoff[6,1]
{txt}
{com}. 
. 
. 
. 
. ********************
. 
. matrix C = J(4, 3, .)
{txt}
{com}. matrix coln C = Point ll95 ul95
{txt}
{com}. matrix rown C = 1 2 3 4
{txt}
{com}. 
. 
. matrix C[1,1] = modelC1zloyaloffon[1,1]
{txt}
{com}. matrix C[1,2] = modelC1zloyaloffon[5,1]
{txt}
{com}. matrix C[1,3] = modelC1zloyaloffon[6,1]
{txt}
{com}. 
. 
. 
. matrix C[2,1] = modelC2zloyaloffon[1,1]
{txt}
{com}. matrix C[2,2] = modelC2zloyaloffon[5,1]
{txt}
{com}. matrix C[2,3] = modelC2zloyaloffon[6,1]
{txt}
{com}. 
. 
. 
. matrix C[3,1] = modelC3zloyaloffon[1,1]
{txt}
{com}. matrix C[3,2] = modelC3zloyaloffon[5,1]
{txt}
{com}. matrix C[3,3] = modelC3zloyaloffon[6,1]
{txt}
{com}. 
. 
. 
. matrix C[4,1] = modelC4zloyaloffon[1,1]
{txt}
{com}. matrix C[4,2] = modelC4zloyaloffon[5,1]
{txt}
{com}. matrix C[4,3] = modelC4zloyaloffon[6,1]
{txt}
{com}. 
. 
. 
. ********************
. 
. matrix D = J(4, 3, .)
{txt}
{com}. matrix coln D = Point ll95 ul95
{txt}
{com}. matrix rown D = 1 2 3 4
{txt}
{com}. 
. matrix D[1,1] = modelC5zloyal[1,1]
{txt}
{com}. matrix D[1,2] = modelC5zloyal[5,1]
{txt}
{com}. matrix D[1,3] = modelC5zloyal[6,1]
{txt}
{com}. 
. 
. matrix D[2,1] = modelC6zloyal[1,1]
{txt}
{com}. matrix D[2,2] = modelC6zloyal[5,1]
{txt}
{com}. matrix D[2,3] = modelC6zloyal[6,1]
{txt}
{com}. 
. 
. matrix D[3,1] = modelC7zloyal[1,1]
{txt}
{com}. matrix D[3,2] = modelC7zloyal[5,1]
{txt}
{com}. matrix D[3,3] = modelC7zloyal[6,1]
{txt}
{com}. 
. 
. matrix D[4,1] = modelC8zloyal[1,1]
{txt}
{com}. matrix D[4,2] = modelC8zloyal[5,1]
{txt}
{com}. matrix D[4,3] = modelC8zloyal[6,1]
{txt}
{com}. 
. 
. 
. coefplot (matrix(A[,1]), mcolor(black) ci((2 3)) ciopts(lpattern(solid) lcolor(black)) label(`""Time of Nomination" "Agenda Status""')) (matrix(B[,1]), mcolor(gs6) ci((2 3)) ciopts(lpattern(dash) lcolor(gs6)) label(`""Switch from On to Off" "Agenda Status""')) (matrix(C[,1]), mcolor(gs10) ci((2 3)) ciopts(lpattern(dash) lcolor(gs10)) label(`""Switch from Off to On" "Agenda Status""')) (matrix(D[,1]), mcolor(gs14) ci((2 3)) ciopts(lpattern(dash) lcolor(gs14)) label(`""Time of Departure" "Agenda Status""')), grid(none) xline(1, lcolor(red%40) lpattern(dash)) xtitle("Hazard Ratio", size(small) margin(t=2)) ylabel(1 "Model C1" 2 "Model C2" 3 "Model C3" 4 "Model C4", labsize(small) noticks) xlabel(0(1)2, angle(0) labsize(small) format(%9.1f)) msymbol(o) mcolor(black) msize(small) title("FIGURE C1", size(small)) ciopts(lcolor(black)) legend(position(3) size(small) col(1) region(lstyle(none))) subtitle("Marginal Differential Effect of Appointee Loyalty on Appointee Tenure Hazard" "[Policy Priority Agencies versus Non-Policy Priority Agencies]", size(small))  mlabel format(%9.3f) mlabposition(12) mlabsize(vsmall)
{res}{txt}
{com}. 
. graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureC1.gph", replace
{txt}(note: file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureC1.gph not found)
{res}{txt}(file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureC1.gph saved)

{com}. 
. 
. 
. 
. 
. 
. 
. 
. ************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. *Figure 2
. 
. matrix E = J(4, 3, .)
{txt}
{com}. matrix coln E = Point ll95 ul95
{txt}
{com}. matrix rown E = 1 2 3 4 
{txt}
{com}. 
. matrix E[1,1] = modelC3azloyalnom[1,1]
{txt}
{com}. matrix E[1,2] = modelC3azloyalnom[5,1]
{txt}
{com}. matrix E[1,3] = modelC3azloyalnom[6,1]
{txt}
{com}. 
. 
. matrix E[2,1] = modelC3bzloyalnom[1,1]
{txt}
{com}. matrix E[2,2] = modelC3bzloyalnom[5,1]
{txt}
{com}. matrix E[2,3] = modelC3bzloyalnom[6,1]
{txt}
{com}. 
. 
. matrix E[3,1] = modelC4azloyalnom[1,1]
{txt}
{com}. matrix E[3,2] = modelC4azloyalnom[5,1]
{txt}
{com}. matrix E[3,3] = modelC4azloyalnom[6,1]
{txt}
{com}. 
. 
. matrix E[4,1] = modelC4bzloyalnom[1,1]
{txt}
{com}. matrix E[4,2] = modelC4bzloyalnom[5,1]
{txt}
{com}. matrix E[4,3] = modelC4bzloyalnom[6,1]
{txt}
{com}. 
. 
. 
. ********************
. 
. matrix F = J(4, 3, .)
{txt}
{com}. matrix coln F = Point ll95 ul95
{txt}
{com}. matrix rown F = 1 2 3 4
{txt}
{com}. 
. matrix F[1,1] = modelC3azloyalonoff[1,1]
{txt}
{com}. matrix F[1,2] = modelC3azloyalonoff[5,1]
{txt}
{com}. matrix F[1,3] = modelC3azloyalonoff[6,1]
{txt}
{com}. 
. 
. matrix F[2,1] = modelC3bzloyalonoff[1,1]
{txt}
{com}. matrix F[2,2] = modelC3bzloyalonoff[5,1]
{txt}
{com}. matrix F[2,3] = modelC3bzloyalonoff[6,1]
{txt}
{com}. 
. 
. matrix F[3,1] = modelC4azloyalonoff[1,1]
{txt}
{com}. matrix F[3,2] = modelC4azloyalonoff[5,1]
{txt}
{com}. matrix F[3,3] = modelC4azloyalonoff[6,1]
{txt}
{com}. 
. 
. matrix F[4,1] = modelC4bzloyalonoff[1,1]
{txt}
{com}. matrix F[4,2] = modelC4bzloyalonoff[5,1]
{txt}
{com}. matrix F[4,3] = modelC4bzloyalonoff[6,1]
{txt}
{com}. 
. 
. ********************
. 
. matrix G = J(4, 3, .)
{txt}
{com}. matrix coln G = Point ll95 ul95
{txt}
{com}. matrix rown G = 1 2 3 4
{txt}
{com}. 
. matrix G[1,1] = modelC3azloyaloffon[1,1]
{txt}
{com}. matrix G[1,2] = modelC3azloyaloffon[5,1]
{txt}
{com}. matrix G[1,3] = modelC3azloyaloffon[6,1]
{txt}
{com}. 
. 
. matrix G[2,1] = modelC3bzloyaloffon[1,1]
{txt}
{com}. matrix G[2,2] = modelC3bzloyaloffon[5,1]
{txt}
{com}. matrix G[2,3] = modelC3bzloyaloffon[6,1]
{txt}
{com}. 
. 
. matrix G[3,1] = modelC4azloyaloffon[1,1]
{txt}
{com}. matrix G[3,2] = modelC4azloyaloffon[5,1]
{txt}
{com}. matrix G[3,3] = modelC4azloyaloffon[6,1]
{txt}
{com}. 
. 
. matrix G[4,1] = modelC4bzloyaloffon[1,1]
{txt}
{com}. matrix G[4,2] = modelC4bzloyaloffon[5,1]
{txt}
{com}. matrix G[4,3] = modelC4bzloyaloffon[6,1]
{txt}
{com}. 
. 
. ********************
. 
. matrix H = J(4, 3, .)
{txt}
{com}. matrix coln H = Point ll95 ul95
{txt}
{com}. matrix rown H = 1 2 3 4
{txt}
{com}. 
. matrix H[1,1] = modelC71azloyal[1,1]
{txt}
{com}. matrix H[1,2] = modelC71azloyal[5,1]
{txt}
{com}. matrix H[1,3] = modelC71azloyal[6,1]
{txt}
{com}. 
. 
. matrix H[2,1] = modelC71bzloyal[1,1]
{txt}
{com}. matrix H[2,2] = modelC71bzloyal[5,1]
{txt}
{com}. matrix H[2,3] = modelC71bzloyal[6,1]
{txt}
{com}. 
. 
. matrix H[3,1] = modelC81azloyal[1,1]
{txt}
{com}. matrix H[3,2] = modelC81azloyal[5,1]
{txt}
{com}. matrix H[3,3] = modelC81azloyal[6,1]
{txt}
{com}. 
. 
. matrix H[4,1] = modelC81bzloyal[1,1]
{txt}
{com}. matrix H[4,2] = modelC81bzloyal[5,1]
{txt}
{com}. matrix H[4,3] = modelC81bzloyal[6,1]
{txt}
{com}. 
. 
. coefplot (matrix(E[,1]), mcolor(black) ci((2 3)) ciopts(lpattern(solid) lcolor(black)) label(`""Time of Nomination" "Agenda Status""')) (matrix(F[,1]), mcolor(gs6) ci((2 3)) ciopts(lpattern(dash) lcolor(gs6)) label(`""Switch from On to Off" "Agenda Status""')) (matrix(G[,1]), mcolor(gs10) ci((2 3)) ciopts(lpattern(dash) lcolor(gs10)) label(`""Switch from Off to On" "Agenda Status""')) (matrix(H[,1]), mcolor(gs14) ci((2 3)) ciopts(lpattern(dash) lcolor(gs14)) label(`""Time of Departure" "Agenda Status""')), grid(none) xtitle("Predicted Number of Days", size(small) margin(t=2)) ylabel(1 `" "Model C3" "Interquartile Change" "' 2 `" "Model C3" "Interdecile Change" "' 3 `" "Model C4" "Interquartile Change" "' 4 `" "Model C4" "Interdecile Change" "', labsize(vsmall) noticks) mlabel format(%9.0f) mlabposition(12) mlabsize(vsmall) xlabel(-400(100)900, angle(0) labsize(small) format(%9.0f))   msymbol(o) mcolor(black) msize(small) title("FIGURE C2", size(small)) ciopts(lcolor(black)) legend(position(3) size(vsmall) col(1) region(lstyle(none))) subtitle("Marginal Differential Effect of Presidential Loyalty on Median Appointee Tenure" "[Policy Priority Agencies versus Non-Policy Priority Agencies]", size(small)) xline(0, lcolor(red%40) lpattern(dash))
{res}{txt}
{com}. 
. graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureC2.gph", replace
{txt}(note: file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureC2.gph not found)
{res}{txt}(file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureC2.gph saved)

{com}. 
. 
. 
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. *** STILL NEED TO MAKE A AIC/BIC Table ***
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Output\Hardwiring Committment.APPENDIX C.04-21-2023.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}22 Apr 2023, 11:23:23
{txt}{.-}
{smcl}
{txt}{sf}{ul off}